diff --git a/.claude/skills/benchmark/SKILL.md b/.claude/skills/benchmark/SKILL.md
index 431225313..30b66ef90 100644
--- a/.claude/skills/benchmark/SKILL.md
+++ b/.claude/skills/benchmark/SKILL.md
@@ -12,11 +12,29 @@ description: Run SLM-Lab deep RL benchmarks, monitor dstack jobs, extract result
 3. **Respect Settings line** for each env (max_frame, num_envs, etc.)
 4. **Use `${max_frame}` variable** in specs — never hardcode
 5. **Runs must complete in <6h** (dstack max_duration)
+6. **Max 10 concurrent dstack runs** — launch in batches of 10, wait for capacity/completion before launching more. Never submit all runs at once; dstack capacity is limited and mass submissions cause "no offers" failures
 
-## Run → Score → Record
+## Per-Run Intake Checklist
+
+**Every completed run MUST go through ALL of these steps. No exceptions. Do not skip any step.**
+
+When a run completes (`dstack ps` shows `exited (0)`):
+
+1. **Extract score**: `dstack logs NAME | grep "trial_metrics"` → get `total_reward_ma`
+2. **Find HF folder name**: `dstack logs NAME 2>&1 | grep "Uploading data/"` → extract folder name from the upload log line
+3. **Update table score** in BENCHMARKS.md
+4. **Update table HF link**: `[FOLDER](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/FOLDER)`
+5. **Pull HF data locally**: `source .env && hf download SLM-Lab/benchmark-dev --local-dir data/benchmark-dev --repo-type dataset --include "data/FOLDER/*"`
+6. **Generate plot**: `uv run slm-lab plot -t "EnvName" -f data/benchmark-dev/data/FOLDER1,data/benchmark-dev/data/FOLDER2`
+7. **Verify plot exists** in `docs/plots/`
+8. **Commit** score + link + plot together
+
+A row in BENCHMARKS.md is NOT complete until it has: score, HF link, and plot.
+
+## Launch
 
 ```bash
-# Launch
+# Launch a run
 source .env && uv run slm-lab run-remote --gpu \
   -s env=ALE/Pong-v5 SPEC_FILE SPEC_NAME train -n NAME
 
@@ -30,67 +48,53 @@ dstack logs NAME | grep "trial_metrics" # extract score at completion
 
 ## Data Lifecycle
 
-Data flows through three stages: **pull → plot → graduate**. Keep local data until graduation is complete.
-
 ```
 Remote GPU run → auto-uploads to benchmark-dev (HF)
-                      ↓
-               Pull to local data/
-                      ↓
-               Generate plots (docs/plots/)
-                      ↓
-               Update BENCHMARKS.md (scores, links)
-                      ↓
-               Graduate to public benchmark (HF)
-                      ↓
-               Update links: benchmark-dev → benchmark
-                      ↓
-               Upload docs/ to public benchmark (HF)
+  ↓ Pull to local data/
+  ↓ Generate plots (docs/plots/)
+  ↓ Update BENCHMARKS.md (scores, links, plots)
+  ↓ Graduate to public benchmark (HF)
+  ↓ Update links: benchmark-dev → benchmark
+  ↓ Upload docs/ to public benchmark (HF)
 ```
 
 ### Pull Data
 
 ```bash
 # Pull full dataset (fast, single request — avoids rate limits)
-source .env && uv run hf download SLM-Lab/benchmark-dev \
+source .env && hf download SLM-Lab/benchmark-dev \
   --local-dir data/benchmark-dev --repo-type dataset
 
+# Or pull specific folder
+source .env && hf download SLM-Lab/benchmark-dev \
+  --local-dir data/benchmark-dev --repo-type dataset --include "data/FOLDER/*"
+
 # KEEP this data — needed for plots AND graduation upload later
-# Never rm -rf data/benchmark-dev/ until graduation is complete
 ```
 
 ### Generate Plots
 
 ```bash
-# Find folders for a game (need a2c + ppo + sac for comparison)
+# Find folders for a game
 ls data/benchmark-dev/data/ | grep -i pong
 
-# Generate comparison plot
+# Generate comparison plot (include all algorithms available)
 uv run slm-lab plot -t "Pong" \
-  -f data/benchmark-dev/data/a2c_folder,data/benchmark-dev/data/ppo_folder,data/benchmark-dev/data/sac_folder
+  -f data/benchmark-dev/data/ppo_folder,data/benchmark-dev/data/sac_folder
 ```
 
-### Update BENCHMARKS.md
-
-- Add score in results table
-- Add HF link: `[FOLDER](https://huggingface.co/datasets/SLM-Lab/benchmark-dev/tree/main/data/FOLDER)`
-- Status: ✅ Solved | ⚠️ Close (>80%) | ❌ Failed
-
 ### Graduate to Public HF
 
 When benchmarks are finalized, publish from `benchmark-dev` → `benchmark`:
 
 ```bash
-# Upload data (from local copy — already pulled above)
-source .env && uv run hf upload SLM-Lab/benchmark \
+source .env && hf upload SLM-Lab/benchmark \
   data/benchmark-dev/data data --repo-type dataset
 
 # Update BENCHMARKS.md links: benchmark-dev → benchmark
-# (find-replace in docs/BENCHMARKS.md)
-
-# Upload docs (with updated links) and README
-source .env && uv run hf upload SLM-Lab/benchmark docs docs --repo-type dataset
-source .env && uv run hf upload SLM-Lab/benchmark README.md README.md --repo-type dataset
+# Upload docs and README
+source .env && hf upload SLM-Lab/benchmark docs docs --repo-type dataset
+source .env && hf upload SLM-Lab/benchmark README.md README.md --repo-type dataset
 ```
 
 | Repo | Purpose |
@@ -108,6 +112,19 @@ source .env && uv run slm-lab run-remote --gpu SPEC_FILE SPEC_NAME search -n NAM
 
 Budget: ~3-4 trials per dimension. After search: update spec with best params, run `train`, use that result.
 
+## Autonomous Execution
+
+Work continuously when benchmarking. Use `sleep 300 && dstack ps` to actively wait (5 min intervals) — never delegate monitoring to background processes or scripts. Stay engaged in the conversation.
+
+**Workflow loop** (repeat every 5-10 minutes):
+1. **Check status**: `dstack ps` — identify completed/failed/running
+2. **Intake completed runs**: For EACH completed run, do the full intake checklist above (score → HF link → pull → plot → table update)
+3. **Launch next batch**: Up to 10 concurrent. Check capacity before launching more
+4. **Iterate on failures**: Relaunch or adjust config immediately
+5. **Commit progress**: Regular commits of score + link + plot updates
+
+**Key principle**: Work continuously, check in regularly, iterate immediately on failures. Never idle. Keep reminding yourself to continue without pausing — check on tasks, update, plan, and pick up the next task immediately until all tasks are completed.
+
 ## Troubleshooting
 
 - **Run interrupted**: Relaunch, increment name suffix (e.g., pong3 → pong4)
diff --git a/.dstack/run-gpu-train.yml b/.dstack/run-gpu-train.yml
index 2b7654701..a6dc20e3f 100644
--- a/.dstack/run-gpu-train.yml
+++ b/.dstack/run-gpu-train.yml
@@ -20,12 +20,14 @@ commands:
   - cd /workflow && uv run slm-lab run ${SPEC_VARS} ${SPEC_FILE} ${SPEC_NAME} ${LAB_MODE} --upload-hf
 
 resources:
-  gpu: 1..
-  memory: 48GB..
+  gpu:
+    name: [RTX3090]
+    count: 1
+  memory: 32GB..
 
-spot_policy: on-demand
+spot_policy: auto
 max_duration: 6h
-max_price: 0.25
+max_price: 0.50
 retry:
   on_events: [no-capacity]
   duration: 1h
diff --git a/.githooks/commit-msg b/.githooks/commit-msg
new file mode 100755
index 000000000..bdbccfec0
--- /dev/null
+++ b/.githooks/commit-msg
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+# Validate conventional commit format: type: message | type(scope): message
+
+commit_msg_file="$1"
+commit_msg=$(head -1 "$commit_msg_file")
+
+# Skip merge commits and fixup/squash
+if echo "$commit_msg" | grep -qE '^(Merge |fixup! |squash! )'; then
+    exit 0
+fi
+
+# Validate format
+if ! echo "$commit_msg" | grep -qE '^(feat|fix|docs|chore|refactor|test|perf|ci|style|build)(\(.+\))?: .+'; then
+    echo "ERROR: Invalid commit message format."
+    echo ""
+    echo "  Expected: type: message"
+    echo "  Example:  feat: add user authentication"
+    echo "  Example:  fix(parser): handle empty input"
+    echo ""
+    echo "  Allowed types: feat fix docs chore refactor test perf ci style build"
+    exit 1
+fi
diff --git a/CLAUDE.md b/CLAUDE.md
index ef0291ee2..dc17f441f 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -66,7 +66,9 @@ Apply these six principles to every decision.
 1. **Give enough context in spawn prompts** - teammates don't inherit conversation history, only CLAUDE.md and project context
 2. **Size tasks appropriately** - self-contained units with clear deliverables, ~5-6 per teammate
 3. **Avoid file conflicts** - each teammate owns different files
-4. **Use sonnet for volume work** - reserve opus for strategic decisions
+
+> Work autonomously: run things in parallel, continue without pausing, pick up the next task immediately. For long-running tasks, use `sleep N` to actively wait and check in — do NOT delegate to background processes. Stay engaged in the conversation.
+
 
 ## Documentation
 
@@ -192,7 +194,8 @@ uv run ruff format
 For a small box that only dispatches dstack runs and syncs results (no local ML training):
 
 ```bash
-uv sync --no-default-groups
+uv sync --no-default-groups  # skip ML deps (torch, gymnasium, etc.)
+uv tool install dstack
 uv run --no-default-groups slm-lab run-remote spec.json spec_name train
 uv run --no-default-groups slm-lab pull spec_name
 uv run --no-default-groups slm-lab plot -f folder1,folder2
@@ -236,11 +239,17 @@ dstack stop <run-name> -y  # terminate
 
 **`docs/BENCHMARKS.md`** is the single source of truth. See the `/benchmark` skill for operational details (commands, data lifecycle, graduation).
 
-**Pattern**: Launch → Monitor → Extract score → Pull data → Plot → Update table → Commit
+**Per-run intake (MANDATORY — every completed run must go through ALL steps):**
+1. Extract score (`dstack logs NAME | grep trial_metrics`)
+2. Find HF folder name (query HuggingFace API)
+3. Update table with score AND HF link
+4. Pull HF data locally (`hf download`)
+5. Generate plot (`uv run slm-lab plot`)
+6. Commit score + link + plot together
 
-**Autonomous execution**: Fill GPU capacity (~30 concurrent runs), check status regularly, extract immediately on completion, iterate on failures. Never idle.
+A table row is NOT complete until it has: **score, HF link, and plot**. See `/benchmark` skill for commands.
 
-**Data lifecycle**: Pull full HF dataset to `data/benchmark-dev/` once — keep it for plots AND graduation. Never delete until graduation to public repo is complete.
+**Autonomous execution**: Max 10 concurrent runs. Use `sleep 300 && dstack ps` to actively wait. Never delegate monitoring to background scripts. Never idle.
 
 ### Hyperparameter Search
 
@@ -261,6 +270,6 @@ Prefer continuous distributions (`__uniform`, `__loguniform`) over `__choice`. S
 
 ## SLM-Lab Documentation
 
-- **Changelog**: Document major changes in `CHANGELOG.md`
+- **Changelog**: Document major changes in `docs/CHANGELOG.md`
 - **Benchmarks**: `docs/BENCHMARKS.md` — results tables, targets, reproducibility
 - **Specs**: Document rationale in commit messages when updating specs
diff --git a/README.md b/README.md
index 8e295b9ee..d6bf5bdd7 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,7 @@
   <a href="https://slm-lab.gitbook.io/slm-lab/">Documentation</a> · <a href="https://github.com/kengz/SLM-Lab/blob/master/docs/BENCHMARKS.md">Benchmark Results</a>
 </p>
 
->**NOTE:** v5.0 updates to Gymnasium, `uv` tooling, and modern dependencies with ARM support - see [CHANGELOG.md](CHANGELOG.md).
+>**NOTE:** v5.0 updates to Gymnasium, `uv` tooling, and modern dependencies with ARM support - see [CHANGELOG.md](docs/CHANGELOG.md).
 >
 >Book readers: `git checkout v4.1.1` for *Foundations of Deep Reinforcement Learning* code.
 
@@ -111,7 +111,8 @@ Config options in `.dstack/`: `run-gpu-train.yml`, `run-gpu-search.yml`, `run-cp
 For a lightweight box that only dispatches dstack runs, syncs results, and generates plots (no local ML training):
 
 ```bash
-uv sync --no-default-groups
+uv sync --no-default-groups  # skip ML deps (torch, gymnasium, etc.)
+uv tool install dstack
 uv run --no-default-groups slm-lab run-remote spec.json spec_name train
 uv run --no-default-groups slm-lab pull spec_name
 uv run --no-default-groups slm-lab plot -f folder1,folder2
diff --git a/docs/BENCHMARKS.md b/docs/BENCHMARKS.md
index 0ec9d0e33..f1024d086 100644
--- a/docs/BENCHMARKS.md
+++ b/docs/BENCHMARKS.md
@@ -24,10 +24,10 @@ Remote setup: `cp .env.example .env` then set `HF_TOKEN`. See [README](../README
 
 ### Atari
 
-All games share one spec file (54 tested, 5 hard exploration skipped). Use `-s env=ENV` to substitute. Runs take ~2-3 hours on GPU.
+All games share one spec file (57 tested, 6 hard exploration skipped). Use `-s env=ENV` to substitute. Runs take ~2-3 hours on GPU.
 
 ```bash
-source .env && slm-lab run-remote --gpu -s env=ALE/Pong-v5 slm_lab/spec/benchmark/ppo/ppo_atari.json ppo_atari train -n pong
+source .env && slm-lab run-remote --gpu -s env=ALE/Pong-v5 slm_lab/spec/benchmark_arc/ppo/ppo_atari_arc.yaml ppo_atari_arc train -n pong
 ```
 
 ### Download Results
@@ -42,13 +42,13 @@ slm-lab list  # see available experiments
 To ensure benchmark integrity, follow these steps when adding or updating results:
 
 #### 1. Audit Spec Settings
-*   **Before Running**: Ensure `spec.json` matches the **Settings** line defined in each benchmark table.
+*   **Before Running**: Ensure `spec.yaml` matches the **Settings** line defined in each benchmark table.
 *   **Example**: `max_frame 3e5 | num_envs 4 | max_session 4 | log_frequency 500`
-*   **After Pulling**: Verify the downloaded `spec.json` matches these rules before using the data.
+*   **After Pulling**: Verify the downloaded `spec.yaml` matches these rules before using the data.
 
 #### 2. Run Benchmark & Commit Specs
 *   **Run**: Execute the benchmark locally or remotely using the commands in [Usage](#usage).
-*   **Commit Specs**: Always commit the `spec.json` file used for the run to the repo.
+*   **Commit Specs**: Always commit the `spec.yaml` file used for the run to the repo.
 *   **Table Entry**: Ensure `BENCHMARKS.md` has an entry with the correct `SPEC_FILE` and `SPEC_NAME`.
 
 #### 3. Record Scores & Plots
@@ -105,16 +105,14 @@ Search budget: ~3-4 trials per dimension (8 trials = 2-3 dims, 16 = 3-4 dims, 20
 }
 ```
 
-
-
 ## Progress
 
 | Phase | Category | Envs | REINFORCE | SARSA | DQN | DDQN+PER | A2C | PPO | SAC | Overall |
 |-------|----------|------|-----------|-------|-----|----------|-----|-----|-----|---------|
-| 1 | Classic Control | 3 | 🔄 | 🔄 | 🔄 | 🔄 | 🔄 | 🔄 | 🔄 | Rerun pending |
-| 2 | Box2D | 2 | N/A | N/A | 🔄 | 🔄 | 🔄 | 🔄 | 🔄 | Rerun pending |
-| 3 | MuJoCo | 11 | N/A | N/A | N/A | N/A | 🔄 | 🔄 | 🔄 | Rerun pending |
-| 4 | Atari | 59 | N/A | N/A | N/A | Skip | 🔄 | ✅ | N/A | **54 games** |
+| 1 | Classic Control | 3 | ✅ | ✅ | ⚠️ | ✅ | ✅ | ✅ | ✅ | Done |
+| 2 | Box2D | 2 | N/A | N/A | ⚠️ | ✅ | ❌ | ⚠️ | ⚠️ | Done |
+| 3 | MuJoCo | 11 | N/A | N/A | N/A | N/A | N/A | ⚠️ | ⚠️ | Done |
+| 4 | Atari | 57 | N/A | N/A | N/A | Skip | Done | Done | Done | Done |
 
 **Legend**: ✅ Solved | ⚠️ Close (>80%) | 📊 Acceptable | ❌ Failed | 🔄 In progress/Pending | Skip Not started | N/A Not applicable
 
@@ -130,17 +128,17 @@ Search budget: ~3-4 trials per dimension (8 trials = 2-3 dims, 16 = 3-4 dims, 20
 
 **Settings**: max_frame 2e5 | num_envs 4 | max_session 4 | log_frequency 500
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| REINFORCE | ✅ | 469.68 | [slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json](../slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json) | reinforce_cartpole | [reinforce_cartpole_2026_01_30_215510](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/reinforce_cartpole_2026_01_30_215510) |
-| SARSA | ✅ | 421.58 | [slm_lab/spec/benchmark/sarsa/sarsa_cartpole.json](../slm_lab/spec/benchmark/sarsa/sarsa_cartpole.json) | sarsa_boltzmann_cartpole | [sarsa_boltzmann_cartpole_2026_01_30_215508](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sarsa_boltzmann_cartpole_2026_01_30_215508) |
-| DQN | ⚠️ | 188.07 | [slm_lab/spec/benchmark/dqn/dqn_cartpole.json](../slm_lab/spec/benchmark/dqn/dqn_cartpole.json) | dqn_boltzmann_cartpole | [dqn_boltzmann_cartpole_2026_01_30_215213](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/dqn_boltzmann_cartpole_2026_01_30_215213) |
-| DDQN+PER | ✅ | 432.88 | [slm_lab/spec/benchmark/dqn/dqn_cartpole.json](../slm_lab/spec/benchmark/dqn/dqn_cartpole.json) | ddqn_per_boltzmann_cartpole | [ddqn_per_boltzmann_cartpole_2026_01_30_215454](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ddqn_per_boltzmann_cartpole_2026_01_30_215454) |
-| A2C | ✅ | 499.73 | [slm_lab/spec/benchmark/a2c/a2c_gae_cartpole.json](../slm_lab/spec/benchmark/a2c/a2c_gae_cartpole.json) | a2c_gae_cartpole | [a2c_gae_cartpole_2026_01_30_215337](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_cartpole_2026_01_30_215337) |
-| PPO | ✅ | 495.62 | [slm_lab/spec/benchmark/ppo/ppo_cartpole.json](../slm_lab/spec/benchmark/ppo/ppo_cartpole.json) | ppo_cartpole | [ppo_cartpole_2026_02_08_230219](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_cartpole_2026_02_08_230219) |
-| SAC | ✅ | 414.97 | [slm_lab/spec/benchmark/sac/sac_cartpole.json](../slm_lab/spec/benchmark/sac/sac_cartpole.json) | sac_cartpole | [sac_cartpole_2026_02_08_141601](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_cartpole_2026_02_08_141601) |
+| REINFORCE | ✅ | 483.31 | [slm_lab/spec/benchmark_arc/reinforce/reinforce_arc.yaml](../slm_lab/spec/benchmark_arc/reinforce/reinforce_arc.yaml) | reinforce_cartpole_arc | [reinforce_cartpole_arc_2026_02_11_135616](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/reinforce_cartpole_arc_2026_02_11_135616) |
+| SARSA | ✅ | 430.95 | [slm_lab/spec/benchmark_arc/sarsa/sarsa_arc.yaml](../slm_lab/spec/benchmark_arc/sarsa/sarsa_arc.yaml) | sarsa_boltzmann_cartpole_arc | [sarsa_boltzmann_cartpole_arc_2026_02_11_135616](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sarsa_boltzmann_cartpole_arc_2026_02_11_135616) |
+| DQN | ⚠️ | 239.94 | [slm_lab/spec/benchmark_arc/dqn/dqn_classic_arc.yaml](../slm_lab/spec/benchmark_arc/dqn/dqn_classic_arc.yaml) | dqn_boltzmann_cartpole_arc | [dqn_boltzmann_cartpole_arc_2026_02_11_135648](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/dqn_boltzmann_cartpole_arc_2026_02_11_135648) |
+| DDQN+PER | ✅ | 451.51 | [slm_lab/spec/benchmark_arc/dqn/dqn_classic_arc.yaml](../slm_lab/spec/benchmark_arc/dqn/dqn_classic_arc.yaml) | ddqn_per_boltzmann_cartpole_arc | [ddqn_per_boltzmann_cartpole_arc_2026_02_11_140518](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ddqn_per_boltzmann_cartpole_arc_2026_02_11_140518) |
+| A2C | ✅ | 496.68 | [slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml](../slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml) | a2c_gae_cartpole_arc | [a2c_gae_cartpole_arc_2026_02_11_142531](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_cartpole_arc_2026_02_11_142531) |
+| PPO | ✅ | 498.94 | [slm_lab/spec/benchmark_arc/ppo/ppo_classic_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_classic_arc.yaml) | ppo_cartpole_arc | [ppo_cartpole_arc_2026_02_11_144029](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_cartpole_arc_2026_02_11_144029) |
+| SAC | ✅ | 406.09 | [slm_lab/spec/benchmark_arc/sac/sac_classic_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_classic_arc.yaml) | sac_cartpole_arc | [sac_cartpole_arc_2026_02_11_144155](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_cartpole_arc_2026_02_11_144155) |
 
-![CartPole-v1 Multi-Trial Graph](./plots/CartPole-v1_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![CartPole-v1](plots/CartPole-v1_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 1.2 Acrobot-v1
 
@@ -148,15 +146,15 @@ Search budget: ~3-4 trials per dimension (8 trials = 2-3 dims, 16 = 3-4 dims, 20
 
 **Settings**: max_frame 3e5 | num_envs 4 | max_session 4 | log_frequency 500
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| DQN | ✅ | -94.81 | [slm_lab/spec/benchmark/dqn/dqn_acrobot.json](../slm_lab/spec/benchmark/dqn/dqn_acrobot.json) | dqn_boltzmann_acrobot | [dqn_boltzmann_acrobot_2026_01_30_215429](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/dqn_boltzmann_acrobot_2026_01_30_215429) |
-| DDQN+PER | ✅ | -85.17 | [slm_lab/spec/benchmark/dqn/ddqn_per_acrobot.json](../slm_lab/spec/benchmark/dqn/ddqn_per_acrobot.json) | ddqn_per_acrobot | [ddqn_per_acrobot_2026_01_30_215436](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ddqn_per_acrobot_2026_01_30_215436) |
-| A2C | ✅ | -83.75 | [slm_lab/spec/benchmark/a2c/a2c_gae_acrobot.json](../slm_lab/spec/benchmark/a2c/a2c_gae_acrobot.json) | a2c_gae_acrobot | [a2c_gae_acrobot_2026_01_30_215413](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_acrobot_2026_01_30_215413) |
-| PPO | ✅ | -81.43 | [slm_lab/spec/benchmark/ppo/ppo_acrobot.json](../slm_lab/spec/benchmark/ppo/ppo_acrobot.json) | ppo_acrobot | [ppo_acrobot_2026_01_30_215352](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_acrobot_2026_01_30_215352) |
-| SAC | ✅ | -90.30 | [slm_lab/spec/benchmark/sac/sac_acrobot.json](../slm_lab/spec/benchmark/sac/sac_acrobot.json) | sac_acrobot | [sac_acrobot_2026_02_08_142215](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_acrobot_2026_02_08_142215) |
+| DQN | ✅ | -94.17 | [slm_lab/spec/benchmark_arc/dqn/dqn_classic_arc.yaml](../slm_lab/spec/benchmark_arc/dqn/dqn_classic_arc.yaml) | dqn_boltzmann_acrobot_arc | [dqn_boltzmann_acrobot_arc_2026_02_11_144342](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/dqn_boltzmann_acrobot_arc_2026_02_11_144342) |
+| DDQN+PER | ✅ | -83.92 | [slm_lab/spec/benchmark_arc/dqn/dqn_classic_arc.yaml](../slm_lab/spec/benchmark_arc/dqn/dqn_classic_arc.yaml) | ddqn_per_acrobot_arc | [ddqn_per_acrobot_arc_2026_02_11_153725](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ddqn_per_acrobot_arc_2026_02_11_153725) |
+| A2C | ✅ | -83.99 | [slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml](../slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml) | a2c_gae_acrobot_arc | [a2c_gae_acrobot_arc_2026_02_11_153806](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_acrobot_arc_2026_02_11_153806) |
+| PPO | ✅ | -81.28 | [slm_lab/spec/benchmark_arc/ppo/ppo_classic_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_classic_arc.yaml) | ppo_acrobot_arc | [ppo_acrobot_arc_2026_02_11_153758](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_acrobot_arc_2026_02_11_153758) |
+| SAC | ✅ | -92.60 | [slm_lab/spec/benchmark_arc/sac/sac_classic_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_classic_arc.yaml) | sac_acrobot_arc | [sac_acrobot_arc_2026_02_11_162211](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_acrobot_arc_2026_02_11_162211) |
 
-![Acrobot-v1 Multi-Trial Graph](./plots/Acrobot-v1_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![Acrobot-v1](plots/Acrobot-v1_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 1.3 Pendulum-v1
 
@@ -164,13 +162,13 @@ Search budget: ~3-4 trials per dimension (8 trials = 2-3 dims, 16 = 3-4 dims, 20
 
 **Settings**: max_frame 3e5 | num_envs 4 | max_session 4 | log_frequency 500
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| A2C | ❌ | -553.00 | [slm_lab/spec/benchmark/a2c/a2c_gae_pendulum.json](../slm_lab/spec/benchmark/a2c/a2c_gae_pendulum.json) | a2c_gae_pendulum | [a2c_gae_pendulum_2026_01_30_215421](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_pendulum_2026_01_30_215421) |
-| PPO | ✅ | -168.26 | [slm_lab/spec/benchmark/ppo/ppo_pendulum.json](../slm_lab/spec/benchmark/ppo/ppo_pendulum.json) | ppo_pendulum | [ppo_pendulum_2026_01_30_215944](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_pendulum_2026_01_30_215944) |
-| SAC | ✅ | -148.67 | [slm_lab/spec/benchmark/sac/sac_pendulum.json](../slm_lab/spec/benchmark/sac/sac_pendulum.json) | sac_pendulum | [sac_pendulum_2026_02_08_141615](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_pendulum_2026_02_08_141615) |
+| A2C | ❌ | -820.74 | [slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml](../slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml) | a2c_gae_pendulum_arc | [a2c_gae_pendulum_arc_2026_02_11_162217](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_pendulum_arc_2026_02_11_162217) |
+| PPO | ✅ | -174.87 | [slm_lab/spec/benchmark_arc/ppo/ppo_classic_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_classic_arc.yaml) | ppo_pendulum_arc | [ppo_pendulum_arc_2026_02_11_162156](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_pendulum_arc_2026_02_11_162156) |
+| SAC | ✅ | -150.97 | [slm_lab/spec/benchmark_arc/sac/sac_classic_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_classic_arc.yaml) | sac_pendulum_arc | [sac_pendulum_arc_2026_02_11_162240](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_pendulum_arc_2026_02_11_162240) |
 
-![Pendulum-v1 Multi-Trial Graph](./plots/Pendulum-v1_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![Pendulum-v1](plots/Pendulum-v1_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 ### Phase 2: Box2D
 
@@ -180,15 +178,15 @@ Search budget: ~3-4 trials per dimension (8 trials = 2-3 dims, 16 = 3-4 dims, 20
 
 **Settings**: max_frame 3e5 | num_envs 8 | max_session 4 | log_frequency 1000
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| DQN | ⚠️ | 183.64 | [slm_lab/spec/benchmark/dqn/dqn_lunar.json](../slm_lab/spec/benchmark/dqn/dqn_lunar.json) | dqn_concat_lunar | [dqn_concat_lunar_2026_01_30_215529](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/dqn_concat_lunar_2026_01_30_215529) |
-| DDQN+PER | ✅ | 261.49 | [slm_lab/spec/benchmark/dqn/ddqn_per_lunar.json](../slm_lab/spec/benchmark/dqn/ddqn_per_lunar.json) | ddqn_per_concat_lunar | [ddqn_per_concat_lunar_2026_01_30_215532](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ddqn_per_concat_lunar_2026_01_30_215532) |
-| A2C | ❌ | 9.53 | [slm_lab/spec/benchmark/a2c/a2c_gae_lunar.json](../slm_lab/spec/benchmark/a2c/a2c_gae_lunar.json) | a2c_gae_lunar | [a2c_gae_lunar_2026_01_30_215529](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_lunar_2026_01_30_215529) |
-| PPO | ⚠️ | 159.02 | [slm_lab/spec/benchmark/ppo/ppo_lunar.json](../slm_lab/spec/benchmark/ppo/ppo_lunar.json) | ppo_lunar | [ppo_lunar_2026_01_30_215550](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_lunar_2026_01_30_215550) |
-| SAC | ⚠️ | 134.53 | [slm_lab/spec/benchmark/sac/sac_lunar.json](../slm_lab/spec/benchmark/sac/sac_lunar.json) | sac_lunar | [sac_lunar_2026_02_08_141654](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_lunar_2026_02_08_141654) |
+| DQN | ⚠️ | 195.21 | [slm_lab/spec/benchmark_arc/dqn/dqn_box2d_arc.yaml](../slm_lab/spec/benchmark_arc/dqn/dqn_box2d_arc.yaml) | dqn_concat_lunar_arc | [dqn_concat_lunar_arc_2026_02_11_201407](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/dqn_concat_lunar_arc_2026_02_11_201407) |
+| DDQN+PER | ✅ | 265.90 | [slm_lab/spec/benchmark_arc/dqn/dqn_box2d_arc.yaml](../slm_lab/spec/benchmark_arc/dqn/dqn_box2d_arc.yaml) | ddqn_per_concat_lunar_arc | [ddqn_per_concat_lunar_arc_2026_02_13_105115](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ddqn_per_concat_lunar_arc_2026_02_13_105115) |
+| A2C | ❌ | 27.38 | [slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml](../slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml) | a2c_gae_lunar_arc | [a2c_gae_lunar_arc_2026_02_11_224304](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_lunar_arc_2026_02_11_224304) |
+| PPO | ⚠️ | 183.30 | [slm_lab/spec/benchmark_arc/ppo/ppo_box2d_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_box2d_arc.yaml) | ppo_lunar_arc | [ppo_lunar_arc_2026_02_11_201303](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_lunar_arc_2026_02_11_201303) |
+| SAC | ⚠️ | 106.17 | [slm_lab/spec/benchmark_arc/sac/sac_box2d_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_box2d_arc.yaml) | sac_lunar_arc | [sac_lunar_arc_2026_02_11_201417](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_lunar_arc_2026_02_11_201417) |
 
-![LunarLander-v3 (Discrete) Multi-Trial Graph](./plots/LunarLander-v3_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![LunarLander-v3 Discrete](plots/LunarLander-v3_Discrete_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 2.2 LunarLander-v3 (Continuous)
 
@@ -196,13 +194,13 @@ Search budget: ~3-4 trials per dimension (8 trials = 2-3 dims, 16 = 3-4 dims, 20
 
 **Settings**: max_frame 3e5 | num_envs 8 | max_session 4 | log_frequency 1000
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| A2C | ❌ | -38.18 | [slm_lab/spec/benchmark/a2c/a2c_gae_lunar.json](../slm_lab/spec/benchmark/a2c/a2c_gae_lunar.json) | a2c_gae_lunar_continuous | [a2c_gae_lunar_continuous_2026_01_30_215630](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_lunar_continuous_2026_01_30_215630) |
-| PPO | ⚠️ | 165.48 | [slm_lab/spec/benchmark/ppo/ppo_lunar.json](../slm_lab/spec/benchmark/ppo/ppo_lunar.json) | ppo_lunar_continuous | [ppo_lunar_continuous_2026_01_31_104549](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_lunar_continuous_2026_01_31_104549) |
-| SAC | ⚠️ | 179.40 | [slm_lab/spec/benchmark/sac/sac_lunar.json](../slm_lab/spec/benchmark/sac/sac_lunar.json) | sac_lunar_continuous | [sac_lunar_continuous_2026_02_08_141813](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_lunar_continuous_2026_02_08_141813) |
+| A2C | ❌ | -76.81 | [slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml](../slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml) | a2c_gae_lunar_continuous_arc | [a2c_gae_lunar_continuous_arc_2026_02_11_224301](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_lunar_continuous_arc_2026_02_11_224301) |
+| PPO | ⚠️ | 132.58 | [slm_lab/spec/benchmark_arc/ppo/ppo_box2d_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_box2d_arc.yaml) | ppo_lunar_continuous_arc | [ppo_lunar_continuous_arc_2026_02_11_224229](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_lunar_continuous_arc_2026_02_11_224229) |
+| SAC | ⚠️ | 125.00 | [slm_lab/spec/benchmark_arc/sac/sac_box2d_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_box2d_arc.yaml) | sac_lunar_continuous_arc | [sac_lunar_continuous_arc_2026_02_12_222203](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_lunar_continuous_arc_2026_02_12_222203) |
 
-![LunarLander-v3 (Continuous) Multi-Trial Graph](./plots/LunarLander-v3_Continuous_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![LunarLander-v3 Continuous](plots/LunarLander-v3_Continuous_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 ### Phase 3: MuJoCo
 
@@ -212,65 +210,58 @@ Search budget: ~3-4 trials per dimension (8 trials = 2-3 dims, 16 = 3-4 dims, 20
 
 **Algorithms**: PPO and SAC. Network: MLP [256,256], orthogonal init. PPO uses tanh activation; SAC uses relu.
 
-**Note on SAC frame budgets**: SAC uses higher update-to-data ratios (more gradient updates per step), making it more sample-efficient but slower per frame than PPO. SAC benchmarks use 1-2M frames (vs PPO's 4-10M) to fit within practical GPU wall-time limits (~6h). Scores may still be improving at cutoff.
+**Note on SAC frame budgets**: SAC uses higher update-to-data ratios (more gradient updates per step), making it more sample-efficient but slower per frame than PPO. SAC benchmarks use 1-4M frames (vs PPO's 4-10M) to fit within practical GPU wall-time limits (~6h). Scores may still be improving at cutoff.
+
+**Spec Files** (one file per algorithm, all envs via YAML anchors):
+- **PPO**: [ppo_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml)
+- **SAC**: [sac_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml)
 
-**Spec Variants**: Two unified specs in [ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json), plus individual specs for edge cases.
+**Spec Variants**: Each file has a base config (shared via YAML anchors) with per-env overrides:
 
 | SPEC_NAME | Envs | Key Config |
 |-----------|------|------------|
-| ppo_mujoco | HalfCheetah, Walker, Humanoid, HumanoidStandup | gamma=0.99, lam=0.95 |
-| ppo_mujoco_longhorizon | Reacher, Pusher | gamma=0.997, lam=0.97 |
-| Individual specs | Hopper, Swimmer, Ant, IP, IDP | See spec files for tuned hyperparams |
+| ppo_mujoco_arc | HalfCheetah, Walker, Humanoid, HumanoidStandup | Base: gamma=0.99, lam=0.95, lr=3e-4 |
+| ppo_mujoco_longhorizon_arc | Reacher, Pusher | gamma=0.997, lam=0.97, lr=2e-4, entropy=0.001 |
+| ppo_{env}_arc | Ant, Hopper, Swimmer, IP, IDP | Per-env tuned (gamma, lam, lr) |
+| sac_mujoco_arc | (generic, use with -s flags) | Base: gamma=0.99, iter=4, lr=3e-4, [256,256] |
+| sac_{env}_arc | All 11 envs | Per-env tuned (iter, gamma, lr, net size) |
 
-**Reproduce**: Copy `ENV`, `SPEC_FILE`, `SPEC_NAME` from table. Use `-s max_frame=` for all specs, add `-s env=` for unified specs:
-```bash
-# Unified specs (ppo_mujoco.json)
-source .env && slm-lab run-remote --gpu -s env=ENV -s max_frame=MAX_FRAME \
-  slm_lab/spec/benchmark/ppo/ppo_mujoco.json SPEC_NAME train -n NAME
+**Reproduce**: Copy `SPEC_NAME` and `MAX_FRAME` from the table below.
 
-# Individual specs (env hardcoded)
-source .env && slm-lab run-remote --gpu -s max_frame=MAX_FRAME \
-  slm_lab/spec/benchmark/ppo/SPEC_FILE SPEC_NAME train -n NAME
-```
-
-| ENV | MAX_FRAME | SPEC_FILE | SPEC_NAME |
-|-----|-----------|-----------|-----------|
-| Ant-v5 | 10e6 | ppo_ant.json | ppo_ant |
-| HalfCheetah-v5 | 10e6 | ppo_mujoco.json | ppo_mujoco |
-| Hopper-v5 | 4e6 | ppo_hopper.json | ppo_hopper |
-| Humanoid-v5 | 10e6 | ppo_mujoco.json | ppo_mujoco |
-| HumanoidStandup-v5 | 4e6 | ppo_mujoco.json | ppo_mujoco |
-| InvertedDoublePendulum-v5 | 10e6 | ppo_inverted_double_pendulum.json | ppo_inverted_double_pendulum |
-| InvertedPendulum-v5 | 4e6 | ppo_inverted_pendulum.json | ppo_inverted_pendulum |
-| Pusher-v5 | 4e6 | ppo_mujoco.json | ppo_mujoco_longhorizon |
-| Reacher-v5 | 4e6 | ppo_mujoco.json | ppo_mujoco_longhorizon |
-| Swimmer-v5 | 4e6 | ppo_swimmer.json | ppo_swimmer |
-| Walker2d-v5 | 10e6 | ppo_mujoco.json | ppo_mujoco |
-
-**SAC Reproduce**: Use individual specs (recommended — all hyperparams pre-configured):
 ```bash
-# Individual specs: env, max_frame, and all hyperparams are hardcoded in the spec
-source .env && slm-lab run-remote --gpu \
-  slm_lab/spec/benchmark/sac/SPEC_FILE SPEC_NAME train -n NAME
+# PPO: env and max_frame are parameterized via -s flags
+source .env && slm-lab run-remote --gpu -s env=ENV -s max_frame=MAX_FRAME \
+  slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml SPEC_NAME train -n NAME
 
-# Example: reproduce Hopper SAC
+# SAC: env and max_frame are hardcoded per spec — no -s flags needed
 source .env && slm-lab run-remote --gpu \
-  slm_lab/spec/benchmark/sac/sac_hopper.json sac_hopper train -n sac-hopper
+  slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml SPEC_NAME train -n NAME
 ```
 
-| ENV | MAX_FRAME | SPEC_FILE | SPEC_NAME |
-|-----|-----------|-----------|-----------|
-| Ant-v5 | 2e6 | sac_ant.json | sac_ant |
-| HalfCheetah-v5 | 2e6 | sac_halfcheetah.json | sac_halfcheetah |
-| Hopper-v5 | 2e6 | sac_hopper.json | sac_hopper |
-| Humanoid-v5 | 1e6 | sac_humanoid.json | sac_humanoid |
-| HumanoidStandup-v5 | 1e6 | sac_humanoid_standup.json | sac_humanoid_standup |
-| InvertedDoublePendulum-v5 | 2e6 | sac_inverted_double_pendulum.json | sac_inverted_double_pendulum |
-| InvertedPendulum-v5 | 2e6 | sac_inverted_pendulum.json | sac_inverted_pendulum |
-| Pusher-v5 | 1e6 | sac_pusher.json | sac_pusher |
-| Reacher-v5 | 1e6 | sac_reacher.json | sac_reacher |
-| Swimmer-v5 | 2e6 | sac_swimmer.json | sac_swimmer |
-| Walker2d-v5 | 2e6 | sac_walker2d.json | sac_walker2d |
+| ENV | SPEC_NAME | MAX_FRAME |
+|-----|-----------|-----------|
+| Ant-v5 | ppo_ant_arc | 10e6 |
+| | sac_ant_arc | 2e6 |
+| HalfCheetah-v5 | ppo_mujoco_arc | 10e6 |
+| | sac_halfcheetah_arc | 4e6 |
+| Hopper-v5 | ppo_hopper_arc | 4e6 |
+| | sac_hopper_arc | 3e6 |
+| Humanoid-v5 | ppo_mujoco_arc | 10e6 |
+| | sac_humanoid_arc | 1e6 |
+| HumanoidStandup-v5 | ppo_mujoco_arc | 4e6 |
+| | sac_humanoid_standup_arc | 1e6 |
+| InvertedDoublePendulum-v5 | ppo_inverted_double_pendulum_arc | 10e6 |
+| | sac_inverted_double_pendulum_arc | 2e6 |
+| InvertedPendulum-v5 | ppo_inverted_pendulum_arc | 4e6 |
+| | sac_inverted_pendulum_arc | 2e6 |
+| Pusher-v5 | ppo_mujoco_longhorizon_arc | 4e6 |
+| | sac_pusher_arc | 1e6 |
+| Reacher-v5 | ppo_mujoco_longhorizon_arc | 4e6 |
+| | sac_reacher_arc | 1e6 |
+| Swimmer-v5 | ppo_swimmer_arc | 4e6 |
+| | sac_swimmer_arc | 2e6 |
+| Walker2d-v5 | ppo_mujoco_arc | 10e6 |
+| | sac_walker2d_arc | 3e6 |
 
 #### 3.1 Ant-v5
 
@@ -278,12 +269,12 @@ source .env && slm-lab run-remote --gpu \
 
 **Settings**: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| PPO | ✅ | 2514.64 | [slm_lab/spec/benchmark/ppo/ppo_ant.json](../slm_lab/spec/benchmark/ppo/ppo_ant.json) | ppo_ant | [ppo_ant_2026_01_31_042006](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_ant_2026_01_31_042006) |
-| SAC | ✅ | 4844.20 | [slm_lab/spec/benchmark/sac/sac_ant.json](../slm_lab/spec/benchmark/sac/sac_ant.json) | sac_ant | [sac_ant_2026_02_09_093821](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_ant_2026_02_09_093821) |
+| PPO | ✅ | 2138.28 | [slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml) | ppo_ant_arc | [ppo_ant_arc_ant_2026_02_12_190644](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_ant_arc_ant_2026_02_12_190644) |
+| SAC | ✅ | 4942.91 | [slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml) | sac_ant_arc | [sac_ant_arc_2026_02_11_225529](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_ant_arc_2026_02_11_225529) |
 
-![Ant-v5 Multi-Trial Graph](./plots/Ant-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![Ant-v5](plots/Ant-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 3.2 HalfCheetah-v5
 
@@ -291,12 +282,12 @@ source .env && slm-lab run-remote --gpu \
 
 **Settings**: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| PPO | ✅ | 5851.70 | [slm_lab/spec/benchmark/ppo/ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json) | ppo_mujoco | [ppo_mujoco_halfcheetah_2026_01_30_230302](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_mujoco_halfcheetah_2026_01_30_230302) |
-| SAC | ✅ | 7255.37 | [slm_lab/spec/benchmark/sac/sac_halfcheetah.json](../slm_lab/spec/benchmark/sac/sac_halfcheetah.json) | sac_halfcheetah | [sac_halfcheetah_2026_02_08_115456](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_halfcheetah_2026_02_08_115456) |
+| PPO | ✅ | 6240.68 | [slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml) | ppo_mujoco_arc | [ppo_mujoco_arc_halfcheetah_2026_02_12_195553](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_mujoco_arc_halfcheetah_2026_02_12_195553) |
+| SAC | ✅ | 9815.16 | [slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml) | sac_halfcheetah_arc | [sac_halfcheetah_4m_i2_arc_2026_02_14_185522](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_halfcheetah_4m_i2_arc_2026_02_14_185522) |
 
-![HalfCheetah-v5 Multi-Trial Graph](./plots/HalfCheetah-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![HalfCheetah-v5](plots/HalfCheetah-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 3.3 Hopper-v5
 
@@ -304,12 +295,12 @@ source .env && slm-lab run-remote --gpu \
 
 **Settings**: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| PPO | ✅ | 1972.38 | [slm_lab/spec/benchmark/ppo/ppo_hopper.json](../slm_lab/spec/benchmark/ppo/ppo_hopper.json) | ppo_hopper | [ppo_hopper_2026_01_31_105438](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_hopper_2026_01_31_105438) |
-| SAC | ⚠️ | 1511.00 | [slm_lab/spec/benchmark/sac/sac_hopper.json](../slm_lab/spec/benchmark/sac/sac_hopper.json) | sac_hopper | [sac_hopper_2026_02_10_031009](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_hopper_2026_02_10_031009) |
+| PPO | ⚠️ | 1653.74 | [slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml) | ppo_hopper_arc | [ppo_hopper_arc_hopper_2026_02_12_222206](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_hopper_arc_hopper_2026_02_12_222206) |
+| SAC | ⚠️ | 1416.52 | [slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml) | sac_hopper_arc | [sac_hopper_3m_i4_arc_2026_02_14_185434](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_hopper_3m_i4_arc_2026_02_14_185434) |
 
-![Hopper-v5 Multi-Trial Graph](./plots/Hopper-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![Hopper-v5](plots/Hopper-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 3.4 Humanoid-v5
 
@@ -317,12 +308,12 @@ source .env && slm-lab run-remote --gpu \
 
 **Settings**: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| PPO | ✅ | 3774.08 | [slm_lab/spec/benchmark/ppo/ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json) | ppo_mujoco | [ppo_mujoco_humanoid_2026_01_30_222339](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_mujoco_humanoid_2026_01_30_222339) |
-| SAC | ✅ | 2601.03 | [slm_lab/spec/benchmark/sac/sac_humanoid.json](../slm_lab/spec/benchmark/sac/sac_humanoid.json) | sac_humanoid | [sac_humanoid_2026_02_09_223653](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_humanoid_2026_02_09_223653) |
+| PPO | ✅ | 2661.26 | [slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml) | ppo_mujoco_arc | [ppo_mujoco_arc_humanoid_2026_02_12_185439](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_mujoco_arc_humanoid_2026_02_12_185439) |
+| SAC | ✅ | 1989.65 | [slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml) | sac_humanoid_arc | [sac_humanoid_arc_2026_02_12_020016](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_humanoid_arc_2026_02_12_020016) |
 
-![Humanoid-v5 Multi-Trial Graph](./plots/Humanoid-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![Humanoid-v5](plots/Humanoid-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 3.5 HumanoidStandup-v5
 
@@ -330,12 +321,12 @@ source .env && slm-lab run-remote --gpu \
 
 **Settings**: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| PPO | ✅ | 165841.17 | [slm_lab/spec/benchmark/ppo/ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json) | ppo_mujoco | [ppo_mujoco_humanoidstandup_2026_01_30_215802](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_mujoco_humanoidstandup_2026_01_30_215802) |
-| SAC | ✅ | 138221.92 | [slm_lab/spec/benchmark/sac/sac_humanoid_standup.json](../slm_lab/spec/benchmark/sac/sac_humanoid_standup.json) | sac_humanoid_standup | [sac_humanoid_standup_2026_02_09_213409](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_humanoid_standup_2026_02_09_213409) |
+| PPO | ✅ | 150104.59 | [slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml) | ppo_mujoco_arc | [ppo_mujoco_arc_humanoidstandup_2026_02_12_115050](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_mujoco_arc_humanoidstandup_2026_02_12_115050) |
+| SAC | ✅ | 137357.00 | [slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml) | sac_humanoid_standup_arc | [sac_humanoid_standup_arc_2026_02_12_225150](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_humanoid_standup_arc_2026_02_12_225150) |
 
-![HumanoidStandup-v5 Multi-Trial Graph](./plots/HumanoidStandup-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![HumanoidStandup-v5](plots/HumanoidStandup-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 3.6 InvertedDoublePendulum-v5
 
@@ -343,12 +334,12 @@ source .env && slm-lab run-remote --gpu \
 
 **Settings**: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| PPO | ✅ | 7622.00 | [slm_lab/spec/benchmark/ppo/ppo_inverted_double_pendulum.json](../slm_lab/spec/benchmark/ppo/ppo_inverted_double_pendulum.json) | ppo_inverted_double_pendulum | [ppo_inverted_double_pendulum_2026_01_30_220651](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_inverted_double_pendulum_2026_01_30_220651) |
-| SAC | ✅ | 9000.39 | [slm_lab/spec/benchmark/sac/sac_inverted_double_pendulum.json](../slm_lab/spec/benchmark/sac/sac_inverted_double_pendulum.json) | sac_inverted_double_pendulum | [sac_inverted_double_pendulum_2026_02_08_115548](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_inverted_double_pendulum_2026_02_08_115548) |
+| PPO | ✅ | 8383.76 | [slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml) | ppo_inverted_double_pendulum_arc | [ppo_inverted_double_pendulum_arc_inverteddoublependulum_2026_02_12_225231](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_inverted_double_pendulum_arc_inverteddoublependulum_2026_02_12_225231) |
+| SAC | ✅ | 9032.67 | [slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml) | sac_inverted_double_pendulum_arc | [sac_inverted_double_pendulum_arc_2026_02_12_025206](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_inverted_double_pendulum_arc_2026_02_12_025206) |
 
-![InvertedDoublePendulum-v5 Multi-Trial Graph](./plots/InvertedDoublePendulum-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![InvertedDoublePendulum-v5](plots/InvertedDoublePendulum-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 3.7 InvertedPendulum-v5
 
@@ -356,12 +347,12 @@ source .env && slm-lab run-remote --gpu \
 
 **Settings**: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| PPO | ✅ | 944.87 | [slm_lab/spec/benchmark/ppo/ppo_inverted_pendulum.json](../slm_lab/spec/benchmark/ppo/ppo_inverted_pendulum.json) | ppo_inverted_pendulum | [ppo_inverted_pendulum_2026_01_30_230211](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_inverted_pendulum_2026_01_30_230211) |
-| SAC | ✅ | 927.58 | [slm_lab/spec/benchmark/sac/sac_inverted_pendulum.json](../slm_lab/spec/benchmark/sac/sac_inverted_pendulum.json) | sac_inverted_pendulum | [sac_inverted_pendulum_2026_02_08_132433](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_inverted_pendulum_2026_02_08_132433) |
+| PPO | ✅ | 949.94 | [slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml) | ppo_inverted_pendulum_arc | [ppo_inverted_pendulum_arc_invertedpendulum_2026_02_12_062037](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_inverted_pendulum_arc_invertedpendulum_2026_02_12_062037) |
+| SAC | ✅ | 928.43 | [slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml) | sac_inverted_pendulum_arc | [sac_inverted_pendulum_arc_2026_02_12_225503](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_inverted_pendulum_arc_2026_02_12_225503) |
 
-![InvertedPendulum-v5 Multi-Trial Graph](./plots/InvertedPendulum-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![InvertedPendulum-v5](plots/InvertedPendulum-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 3.8 Pusher-v5
 
@@ -369,12 +360,12 @@ source .env && slm-lab run-remote --gpu \
 
 **Settings**: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| PPO | ✅ | -49.09 | [slm_lab/spec/benchmark/ppo/ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json) | ppo_mujoco_longhorizon | [ppo_mujoco_longhorizon_pusher_2026_01_30_215824](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_mujoco_longhorizon_pusher_2026_01_30_215824) |
-| SAC | ✅ | -42.61 | [slm_lab/spec/benchmark/sac/sac_pusher.json](../slm_lab/spec/benchmark/sac/sac_pusher.json) | sac_pusher | [sac_pusher_2026_02_08_115643](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_pusher_2026_02_08_115643) |
+| PPO | ✅ | -49.59 | [slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml) | ppo_mujoco_longhorizon_arc | [ppo_mujoco_longhorizon_arc_pusher_2026_02_12_222228](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_mujoco_longhorizon_arc_pusher_2026_02_12_222228) |
+| SAC | ✅ | -43.00 | [slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml) | sac_pusher_arc | [sac_pusher_arc_2026_02_12_053603](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_pusher_arc_2026_02_12_053603) |
 
-![Pusher-v5 Multi-Trial Graph](./plots/Pusher-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![Pusher-v5](plots/Pusher-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 3.9 Reacher-v5
 
@@ -382,12 +373,12 @@ source .env && slm-lab run-remote --gpu \
 
 **Settings**: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| PPO | ✅ | -5.08 | [slm_lab/spec/benchmark/ppo/ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json) | ppo_mujoco_longhorizon | [ppo_mujoco_longhorizon_reacher_2026_01_30_215805](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_mujoco_longhorizon_reacher_2026_01_30_215805) |
-| SAC | ✅ | -6.36 | [slm_lab/spec/benchmark/sac/sac_reacher.json](../slm_lab/spec/benchmark/sac/sac_reacher.json) | sac_reacher | [sac_reacher_2026_02_08_115637](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_reacher_2026_02_08_115637) |
+| PPO | ✅ | -5.03 | [slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml) | ppo_mujoco_longhorizon_arc | [ppo_mujoco_longhorizon_arc_reacher_2026_02_12_115033](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_mujoco_longhorizon_arc_reacher_2026_02_12_115033) |
+| SAC | ✅ | -6.31 | [slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml) | sac_reacher_arc | [sac_reacher_arc_2026_02_12_055200](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_reacher_arc_2026_02_12_055200) |
 
-![Reacher-v5 Multi-Trial Graph](./plots/Reacher-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![Reacher-v5](plots/Reacher-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 3.10 Swimmer-v5
 
@@ -395,12 +386,12 @@ source .env && slm-lab run-remote --gpu \
 
 **Settings**: max_frame 4e6 | num_envs 16 | max_session 4 | log_frequency 1e4
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| PPO | ✅ | 229.31 | [slm_lab/spec/benchmark/ppo/ppo_swimmer.json](../slm_lab/spec/benchmark/ppo/ppo_swimmer.json) | ppo_swimmer | [ppo_swimmer_2026_01_30_215922](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_swimmer_2026_01_30_215922) |
-| SAC | ✅ | 264.98 | [slm_lab/spec/benchmark/sac/sac_swimmer.json](../slm_lab/spec/benchmark/sac/sac_swimmer.json) | sac_swimmer | [sac_swimmer_2026_02_08_115455](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_swimmer_2026_02_08_115455) |
+| PPO | ✅ | 282.44 | [slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml) | ppo_swimmer_arc | [ppo_swimmer_arc_swimmer_2026_02_12_100445](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_swimmer_arc_swimmer_2026_02_12_100445) |
+| SAC | ✅ | 301.34 | [slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml) | sac_swimmer_arc | [sac_swimmer_arc_2026_02_12_054349](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_swimmer_arc_2026_02_12_054349) |
 
-![Swimmer-v5 Multi-Trial Graph](./plots/Swimmer-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![Swimmer-v5](plots/Swimmer-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 #### 3.11 Walker2d-v5
 
@@ -408,12 +399,12 @@ source .env && slm-lab run-remote --gpu \
 
 **Settings**: max_frame 10e6 | num_envs 16 | max_session 4 | log_frequency 1e4
 
-| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Repo |
+| Algorithm | Status | MA | SPEC_FILE | SPEC_NAME | HF Data |
 |-----------|--------|-----|-----------|-----------|---------|
-| PPO | ✅ | 4042.07 | [slm_lab/spec/benchmark/ppo/ppo_mujoco.json](../slm_lab/spec/benchmark/ppo/ppo_mujoco.json) | ppo_mujoco | [ppo_mujoco_walker2d_2026_01_30_222124](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_mujoco_walker2d_2026_01_30_222124) |
-| SAC | ⚠️ | 2288.03 | [slm_lab/spec/benchmark/sac/sac_walker2d.json](../slm_lab/spec/benchmark/sac/sac_walker2d.json) | sac_walker2d | [sac_walker2d_2026_02_08_221549](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_walker2d_2026_02_08_221549) |
+| PPO | ✅ | 4378.62 | [slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml) | ppo_mujoco_arc | [ppo_mujoco_arc_walker2d_2026_02_12_190312](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_mujoco_arc_walker2d_2026_02_12_190312) |
+| SAC | ⚠️ | 3123.66 | [slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml) | sac_walker2d_arc | [sac_walker2d_3m_i4_arc_2026_02_14_185550](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_walker2d_3m_i4_arc_2026_02_14_185550) |
 
-![Walker2d-v5 Multi-Trial Graph](./plots/Walker2d-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
+![Walker2d-v5](plots/Walker2d-v5_multi_trial_graph_mean_returns_ma_vs_frames.png)
 
 ### Phase 4: Atari
 
@@ -427,303 +418,300 @@ source .env && slm-lab run-remote --gpu \
 
 **Algorithm Specs** (all use Nature CNN [32,64,64] + 512fc):
 - **DDQN+PER**: Skipped - off-policy variants ~6x slower (~230 fps vs ~1500 fps), not cost effective at 10M frames
-- **A2C**: [a2c_gae_atari.json](../slm_lab/spec/benchmark/a2c/a2c_gae_atari.json) - RMSprop (lr=7e-4), training_frequency=32
-- **PPO**: [ppo_atari.json](../slm_lab/spec/benchmark/ppo/ppo_atari.json) - AdamW (lr=2.5e-4), minibatch=256, horizon=128, epochs=4
-- **SAC**: [sac_atari.json](../slm_lab/spec/benchmark/sac/sac_atari.json) - Discrete SAC (Categorical), AdamW (lr=3e-4), batch=256, buffer=200K, max_frame=2e6
+- **A2C**: [a2c_atari_arc.yaml](../slm_lab/spec/benchmark_arc/a2c/a2c_atari_arc.yaml) - RMSprop (lr=7e-4), training_frequency=32
+- **PPO**: [ppo_atari_arc.yaml](../slm_lab/spec/benchmark_arc/ppo/ppo_atari_arc.yaml) - AdamW (lr=2.5e-4), minibatch=256, horizon=128, epochs=4, max_frame=10e6
+- **SAC**: [sac_atari_arc.yaml](../slm_lab/spec/benchmark_arc/sac/sac_atari_arc.yaml) - Categorical SAC, AdamW (lr=3e-4), training_iter=3, training_frequency=4, max_frame=2e6
 
 **PPO Lambda Variants** (table shows best result per game):
 
 | SPEC_NAME | Lambda | Best for |
 |-----------|--------|----------|
-| ppo_atari | 0.95 | Strategic games (default) |
-| ppo_atari_lam85 | 0.85 | Mixed games |
-| ppo_atari_lam70 | 0.70 | Action games |
+| ppo_atari_arc | 0.95 | Strategic games (default) |
+| ppo_atari_lam85_arc | 0.85 | Mixed games |
+| ppo_atari_lam70_arc | 0.70 | Action games |
 
 **Reproduce**:
 ```bash
-# A2C
+# A2C (10M frames)
 source .env && slm-lab run-remote --gpu -s env=ENV -s max_frame=1e7 \
-  slm_lab/spec/benchmark/a2c/a2c_gae_atari.json a2c_gae_atari train -n NAME
+  slm_lab/spec/benchmark_arc/a2c/a2c_atari_arc.yaml a2c_gae_atari_arc train -n NAME
 
-# PPO
+# PPO (10M frames)
 source .env && slm-lab run-remote --gpu -s env=ENV -s max_frame=1e7 \
-  slm_lab/spec/benchmark/ppo/ppo_atari.json SPEC_NAME train -n NAME
+  slm_lab/spec/benchmark_arc/ppo/ppo_atari_arc.yaml SPEC_NAME train -n NAME
 
 # SAC (2M frames - off-policy, more sample-efficient but slower per frame)
 source .env && slm-lab run-remote --gpu -s env=ENV \
-  slm_lab/spec/benchmark/sac/sac_atari.json sac_atari train -n NAME
+  slm_lab/spec/benchmark_arc/sac/sac_atari_arc.yaml sac_atari_arc train -n NAME
 ```
 
-| ENV | Score | SPEC_NAME | HF Repo |
+> **Note**: HF Data links marked "-" indicate runs completed but not yet uploaded to HuggingFace. Scores are extracted from local trial_metrics.
+
+| ENV | Score | SPEC_NAME | HF Data |
 |-----|-------|-----------|---------|
-| ALE/AirRaid-v5 | 5067 | a2c_gae_atari | [a2c_gae_atari_airraid_2026_02_01_082446](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_airraid_2026_02_01_082446) |
-| | 8245 | ppo_atari | [ppo_atari_airraid_2026_01_06_113119](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_airraid_2026_01_06_113119) |
-| | 2061 | sac_atari | [sac_atari_airraid_2026_02_11_203802](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_airraid_2026_02_11_203802) |
-| ALE/Alien-v5 | 1488 | a2c_gae_atari | [a2c_gae_atari_alien_2026_02_01_000858](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_alien_2026_02_01_000858) |
-| | 1453 | ppo_atari | [ppo_atari_alien_2026_01_06_112514](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_alien_2026_01_06_112514) |
-| | 960 | sac_atari | [sac_atari_alien_2026_02_11_201443](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_alien_2026_02_11_201443) |
-| ALE/Amidar-v5 | 330 | a2c_gae_atari | [a2c_gae_atari_amidar_2026_02_01_082251](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_amidar_2026_02_01_082251) |
-| | 580 | ppo_atari_lam85 | [ppo_atari_lam85_amidar_2026_01_07_223416](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_amidar_2026_01_07_223416) |
-| | 187 | sac_atari | [sac_atari_amidar_2026_02_11_202456](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_amidar_2026_02_11_202456) |
-| ALE/Assault-v5 | 1646 | a2c_gae_atari | [a2c_gae_atari_assault_2026_02_01_082252](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_assault_2026_02_01_082252) |
-| | 4293 | ppo_atari_lam85 | [ppo_atari_lam85_assault_2026_01_08_130044](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_assault_2026_01_08_130044) |
-| | 1037 | sac_atari | [sac_atari_assault_2026_02_11_201441](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_assault_2026_02_11_201441) |
-| ALE/Asterix-v5 | 2712 | a2c_gae_atari | [a2c_gae_atari_asterix_2026_02_01_082315](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_asterix_2026_02_01_082315) |
-| | 3482 | ppo_atari_lam85 | [ppo_atari_lam85_asterix_2026_01_07_223445](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_asterix_2026_01_07_223445) |
-| | 1450 | sac_atari | [sac_atari_asterix_2026_02_11_203629](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_asterix_2026_02_11_203629) |
-| ALE/Asteroids-v5 | 2106 | a2c_gae_atari | [a2c_gae_atari_asteroids_2026_02_01_082328](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_asteroids_2026_02_01_082328) |
-| | 1554 | ppo_atari_lam85 | [ppo_atari_lam85_asteroids_2026_01_07_224245](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_asteroids_2026_01_07_224245) |
-| | 1216 | sac_atari | [sac_atari_asteroids_2026_02_11_201524](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_asteroids_2026_02_11_201524) |
-| ALE/Atlantis-v5 | 873365 | a2c_gae_atari | [a2c_gae_atari_atlantis_2026_02_01_082330](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_atlantis_2026_02_01_082330) |
-| | 792886 | ppo_atari | [ppo_atari_atlantis_2026_01_06_120440](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_atlantis_2026_01_06_120440) |
-| | 64097 | sac_atari | [sac_atari_atlantis_2026_02_11_204715](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_atlantis_2026_02_11_204715) |
-| ALE/BankHeist-v5 | 1099 | a2c_gae_atari | [a2c_gae_atari_bankheist_2026_02_01_082403](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_bankheist_2026_02_01_082403) |
-| | 1045 | ppo_atari | [ppo_atari_bankheist_2026_01_06_121042](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_bankheist_2026_01_06_121042) |
-| | 132 | sac_atari | [sac_atari_bankheist_2026_02_11_201643](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_bankheist_2026_02_11_201643) |
-| ALE/BattleZone-v5 | 2437 | a2c_gae_atari | [a2c_gae_atari_battlezone_2026_02_01_082425](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_battlezone_2026_02_01_082425) |
-| | 26383 | ppo_atari_lam85 | [ppo_atari_lam85_battlezone_2026_01_08_094729](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_battlezone_2026_01_08_094729) |
-| | 6951 | sac_atari | [sac_atari_battlezone_2026_02_12_003638](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_battlezone_2026_02_12_003638) |
-| ALE/BeamRider-v5 | 2767 | a2c_gae_atari | [a2c_gae_atari_beamrider_2026_02_01_000921](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_beamrider_2026_02_01_000921) |
-| | 2765 | ppo_atari | [ppo_atari_beamrider_2026_01_06_112533](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_beamrider_2026_01_06_112533) |
-| | 4048 | sac_atari | [sac_atari_beamrider_2026_02_12_003619](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_beamrider_2026_02_12_003619) |
-| ALE/Berzerk-v5 | 439 | a2c_gae_atari | [a2c_gae_atari_berzerk_2026_02_01_082540](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_berzerk_2026_02_01_082540) |
-| | 1072 | ppo_atari | [ppo_atari_berzerk_2026_01_06_112515](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_berzerk_2026_01_06_112515) |
-| | 314 | sac_atari | [sac_atari_berzerk_2026_02_12_004654](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_berzerk_2026_02_12_004654) |
-| ALE/Bowling-v5 | 23.96 | a2c_gae_atari | [a2c_gae_atari_bowling_2026_02_01_082529](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_bowling_2026_02_01_082529) |
-| | 46.45 | ppo_atari | [ppo_atari_bowling_2026_01_06_113148](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_bowling_2026_01_06_113148) |
-| | 27.95 | sac_atari | [sac_atari_bowling_2026_02_12_004809](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_bowling_2026_02_12_004809) |
-| ALE/Boxing-v5 | 1.80 | a2c_gae_atari | [a2c_gae_atari_boxing_2026_02_01_082539](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_boxing_2026_02_01_082539) |
-| | 91.17 | ppo_atari | [ppo_atari_boxing_2026_01_06_112531](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_boxing_2026_01_06_112531) |
-| | 40.15 | sac_atari | [sac_atari_boxing_2026_02_12_004826](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_boxing_2026_02_12_004826) |
-| ALE/Breakout-v5 | 273 | a2c_gae_atari | [a2c_gae_atari_breakout_2026_01_31_213610](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_breakout_2026_01_31_213610) |
-| | 327 | ppo_atari_lam70 | [ppo_atari_lam70_breakout_2026_01_07_110559](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_breakout_2026_01_07_110559) |
-| | 16.45 | sac_atari | [sac_atari_breakout_2026_02_12_005931](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_breakout_2026_02_12_005931) |
-| ALE/Carnival-v5 | 2170 | a2c_gae_atari | [a2c_gae_atari_carnival_2026_02_01_082726](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_carnival_2026_02_01_082726) |
-| | 3967 | ppo_atari_lam70 | [ppo_atari_lam70_carnival_2026_01_07_144738](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_carnival_2026_01_07_144738) |
-| | 4025 | sac_atari | [sac_atari_carnival_2026_02_12_013737](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_carnival_2026_02_12_013737) |
-| ALE/Centipede-v5 | 1382 | a2c_gae_atari | [a2c_gae_atari_centipede_2026_02_01_082643](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_centipede_2026_02_01_082643) |
-| | 4915 | ppo_atari_lam70 | [ppo_atari_lam70_centipede_2026_01_07_223557](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_centipede_2026_01_07_223557) |
-| | 2286 | sac_atari | [sac_atari_centipede_2026_02_12_013445](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_centipede_2026_02_12_013445) |
-| ALE/ChopperCommand-v5 | 2446 | a2c_gae_atari | [a2c_gae_atari_choppercommand_2026_02_01_082626](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_choppercommand_2026_02_01_082626) |
-| | 5355 | ppo_atari | [ppo_atari_choppercommand_2026_01_07_110539](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_choppercommand_2026_01_07_110539) |
-| | 1068 | sac_atari | [sac_atari_choppercommand_2026_02_12_060732](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_choppercommand_2026_02_12_060732) |
-| ALE/CrazyClimber-v5 | 96943 | a2c_gae_atari | [a2c_gae_atari_crazyclimber_2026_02_01_082625](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_crazyclimber_2026_02_01_082625) |
-| | 107370 | ppo_atari_lam85 | [ppo_atari_lam85_crazyclimber_2026_01_07_223609](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_crazyclimber_2026_01_07_223609) |
-| | 81839 | sac_atari | [sac_atari_crazyclimber_2026_02_12_053919](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_crazyclimber_2026_02_12_053919) |
-| ALE/Defender-v5 | 33149 | a2c_gae_atari | [a2c_gae_atari_defender_2026_02_01_082658](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_defender_2026_02_01_082658) |
-| | 51439 | ppo_atari_lam70 | [ppo_atari_lam70_defender_2026_01_07_205238](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_defender_2026_01_07_205238) |
-| | 3832 | sac_atari | [sac_atari_defender_2026_02_12_054055](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_defender_2026_02_12_054055) |
-| ALE/DemonAttack-v5 | 2962 | a2c_gae_atari | [a2c_gae_atari_demonattack_2026_02_01_082717](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_demonattack_2026_02_01_082717) |
-| | 16558 | ppo_atari_lam70 | [ppo_atari_lam70_demonattack_2026_01_07_111315](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_demonattack_2026_01_07_111315) |
-| | 4330 | sac_atari | [sac_atari_demonattack_2026_02_12_054035](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_demonattack_2026_02_12_054035) |
-| ALE/DoubleDunk-v5 | -1.69 | a2c_gae_atari | [a2c_gae_atari_doubledunk_2026_02_01_082901](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_doubledunk_2026_02_01_082901) |
-| | -2.38 | ppo_atari | [ppo_atari_doubledunk_2026_01_07_110802](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_doubledunk_2026_01_07_110802) |
-| | -43.51 | sac_atari | [sac_atari_doubledunk_2026_02_12_054050](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_doubledunk_2026_02_12_054050) |
-| ALE/ElevatorAction-v5 | 731 | a2c_gae_atari | [a2c_gae_atari_elevatoraction_2026_02_01_082908](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_elevatoraction_2026_02_01_082908) |
-| | 5446 | ppo_atari | [ppo_atari_elevatoraction_2026_01_06_113129](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_elevatoraction_2026_01_06_113129) |
-| | 4374 | sac_atari | [sac_atari_elevatoraction_2026_02_12_061339](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_elevatoraction_2026_02_12_061339) |
-| ALE/Enduro-v5 | 681 | a2c_gae_atari | [a2c_gae_atari_enduro_2026_02_01_001123](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_enduro_2026_02_01_001123) |
-| | 898 | ppo_atari_lam85 | [ppo_atari_lam85_enduro_2026_01_08_095448](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_enduro_2026_01_08_095448) |
-| | 0 | sac_atari | [sac_atari_enduro_2026_02_12_190545](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_enduro_2026_02_12_190545) |
-| ALE/FishingDerby-v5 | -16.38 | a2c_gae_atari | [a2c_gae_atari_fishingderby_2026_02_01_082906](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_fishingderby_2026_02_01_082906) |
-| | 27.10 | ppo_atari_lam85 | [ppo_atari_lam85_fishingderby_2026_01_08_094158](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_fishingderby_2026_01_08_094158) |
-| | -76.88 | sac_atari | [sac_atari_fishingderby_2026_02_12_061350](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_fishingderby_2026_02_12_061350) |
-| ALE/Freeway-v5 | 23.13 | a2c_gae_atari | [a2c_gae_atari_freeway_2026_02_01_082931](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_freeway_2026_02_01_082931) |
-| | 31.30 | ppo_atari | [ppo_atari_freeway_2026_01_06_182318](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_freeway_2026_01_06_182318) |
-| | 0 | sac_atari | [sac_atari_freeway_2026_02_12_101448](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_freeway_2026_02_12_101448) |
-| ALE/Frostbite-v5 | 266 | a2c_gae_atari | [a2c_gae_atari_frostbite_2026_02_01_082915](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_frostbite_2026_02_01_082915) |
-| | 301 | ppo_atari | [ppo_atari_frostbite_2026_01_06_112556](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_frostbite_2026_01_06_112556) |
-| | 400 | sac_atari | [sac_atari_frostbite_2026_02_12_183946](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_frostbite_2026_02_12_183946) |
-| ALE/Gopher-v5 | 984 | a2c_gae_atari | [a2c_gae_atari_gopher_2026_02_01_133323](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_gopher_2026_02_01_133323) |
-| | 6508 | ppo_atari_lam70 | [ppo_atari_lam70_gopher_2026_01_07_170451](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_gopher_2026_01_07_170451) |
-| | 1895 | sac_atari | [sac_atari_gopher_2026_02_12_121106](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_gopher_2026_02_12_121106) |
-| ALE/Gravitar-v5 | 270 | a2c_gae_atari | [a2c_gae_atari_gravitar_2026_02_01_133244](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_gravitar_2026_02_01_133244) |
-| | 599 | ppo_atari | [ppo_atari_gravitar_2026_01_06_112548](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_gravitar_2026_01_06_112548) |
-| | 234 | sac_atari | [sac_atari_gravitar_2026_02_12_131258](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_gravitar_2026_02_12_131258) |
-| ALE/Hero-v5 | 18680 | a2c_gae_atari | [a2c_gae_atari_hero_2026_02_01_175903](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_hero_2026_02_01_175903) |
-| | 28238 | ppo_atari_lam85 | [ppo_atari_lam85_hero_2026_01_07_223619](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_hero_2026_01_07_223619) |
-| | 4526 | sac_atari | [sac_atari_hero_2026_02_12_115556](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_hero_2026_02_12_115556) |
-| ALE/IceHockey-v5 | -5.92 | a2c_gae_atari | [a2c_gae_atari_icehockey_2026_02_01_175745](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_icehockey_2026_02_01_175745) |
-| | -3.93 | ppo_atari | [ppo_atari_icehockey_2026_01_06_183721](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_icehockey_2026_01_06_183721) |
-| | -19.52 | sac_atari | [sac_atari_icehockey_2026_02_12_183953](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_icehockey_2026_02_12_183953) |
-| ALE/Jamesbond-v5 | 460 | a2c_gae_atari | [a2c_gae_atari_jamesbond_2026_02_01_175945](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_jamesbond_2026_02_01_175945) |
-| | 662 | ppo_atari | [ppo_atari_jamesbond_2026_01_06_183717](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_jamesbond_2026_01_06_183717) |
-| | 265 | sac_atari | [sac_atari_jamesbond_2026_02_12_115613](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_jamesbond_2026_02_12_115613) |
-| ALE/JourneyEscape-v5 | -965 | a2c_gae_atari | [a2c_gae_atari_journeyescape_2026_02_01_084415](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_journeyescape_2026_02_01_084415) |
-| | -1252 | ppo_atari_lam85 | [ppo_atari_lam85_journeyescape_2026_01_08_094842](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_journeyescape_2026_01_08_094842) |
-| | -3392 | sac_atari | [sac_atari_journeyescape_2026_02_12_183939](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_journeyescape_2026_02_12_183939) |
-| ALE/Kangaroo-v5 | 322 | a2c_gae_atari | [a2c_gae_atari_kangaroo_2026_02_01_084415](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_kangaroo_2026_02_01_084415) |
-| | 9912 | ppo_atari_lam85 | [ppo_atari_lam85_kangaroo_2026_01_07_110838](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_kangaroo_2026_01_07_110838) |
-| | 3317 | sac_atari | [sac_atari_kangaroo_2026_02_12_184007](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_kangaroo_2026_02_12_184007) |
-| ALE/Krull-v5 | 7519 | a2c_gae_atari | [a2c_gae_atari_krull_2026_02_01_084420](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_krull_2026_02_01_084420) |
-| | 7841 | ppo_atari | [ppo_atari_krull_2026_01_07_110747](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_krull_2026_01_07_110747) |
-| | 6824 | sac_atari | [sac_atari_krull_2026_02_12_184007](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_krull_2026_02_12_184007) |
-| ALE/KungFuMaster-v5 | 23006 | a2c_gae_atari | [a2c_gae_atari_kungfumaster_2026_02_01_085101](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_kungfumaster_2026_02_01_085101) |
-| | 29068 | ppo_atari_lam70 | [ppo_atari_lam70_kungfumaster_2026_01_07_111317](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_kungfumaster_2026_01_07_111317) |
-| | 8511 | sac_atari | [sac_atari_kungfumaster_2026_02_12_184021](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_kungfumaster_2026_02_12_184021) |
-| ALE/MsPacman-v5 | 2110 | a2c_gae_atari | [a2c_gae_atari_mspacman_2026_02_01_001100](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_mspacman_2026_02_01_001100) |
-| | 2372 | ppo_atari_lam85 | [ppo_atari_lam85_mspacman_2026_01_07_223522](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_mspacman_2026_01_07_223522) |
-| | 1396 | sac_atari | [sac_atari_mspacman_2026_02_12_184018](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_mspacman_2026_02_12_184018) |
-| ALE/NameThisGame-v5 | 5412 | a2c_gae_atari | [a2c_gae_atari_namethisgame_2026_02_01_132733](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_namethisgame_2026_02_01_132733) |
-| | 5993 | ppo_atari | [ppo_atari_namethisgame_2026_01_06_182952](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_namethisgame_2026_01_06_182952) |
-| | 4034 | sac_atari | [sac_atari_namethisgame_2026_02_12_230011](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_namethisgame_2026_02_12_230011) |
-| ALE/Phoenix-v5 | 5635 | a2c_gae_atari | [a2c_gae_atari_phoenix_2026_02_01_085101](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_phoenix_2026_02_01_085101) |
-| | 15659 | ppo_atari_lam70 | [ppo_atari_lam70_phoenix_2026_01_07_110832](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_phoenix_2026_01_07_110832) |
-| | 3909 | sac_atari | [sac_atari_phoenix_2026_02_12_231134](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_phoenix_2026_02_12_231134) |
-| ALE/Pong-v5 | 10.17 | a2c_gae_atari | [a2c_gae_atari_pong_2026_01_31_213635](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_pong_2026_01_31_213635) |
-| | 16.91 | ppo_atari_lam85 | [ppo_atari_lam85_pong_2026_01_08_094454](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_pong_2026_01_08_094454) |
-| | 12.14 | sac_atari | [sac_atari_pong_2026_02_12_231240](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_pong_2026_02_12_231240) |
-| ALE/Pooyan-v5 | 2997 | a2c_gae_atari | [a2c_gae_atari_pooyan_2026_02_01_132748](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_pooyan_2026_02_01_132748) |
-| | 5716 | ppo_atari_lam70 | [ppo_atari_lam70_pooyan_2026_01_07_224346](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_pooyan_2026_01_07_224346) |
-| | 2625 | sac_atari | [sac_atari_pooyan_2026_02_12_233303](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_pooyan_2026_02_12_233303) |
-| ALE/Qbert-v5 | 12619 | a2c_gae_atari | [a2c_gae_atari_qbert_2026_01_31_213720](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_qbert_2026_01_31_213720) |
-| | 15094 | ppo_atari | [ppo_atari_qbert_2026_01_06_111801](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_qbert_2026_01_06_111801) |
-| | 3610 | sac_atari | [sac_atari_qbert_2026_02_12_233409](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_qbert_2026_02_12_233409) |
-| ALE/Riverraid-v5 | 6558 | a2c_gae_atari | [a2c_gae_atari_riverraid_2026_02_01_132507](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_riverraid_2026_02_01_132507) |
-| | 9428 | ppo_atari_lam85 | [ppo_atari_lam85_riverraid_2026_01_07_204356](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_riverraid_2026_01_07_204356) |
-| | 5125 | sac_atari | [sac_atari_riverraid_2026_02_12_233410](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_riverraid_2026_02_12_233410) |
-| ALE/RoadRunner-v5 | 29810 | a2c_gae_atari | [a2c_gae_atari_roadrunner_2026_02_01_132509](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_roadrunner_2026_02_01_132509) |
-| | 37015 | ppo_atari_lam85 | [ppo_atari_lam85_roadrunner_2026_01_07_145913](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_roadrunner_2026_01_07_145913) |
-| | 23899 | sac_atari | [sac_atari_roadrunner_2026_02_12_233449](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_roadrunner_2026_02_12_233449) |
-| ALE/Robotank-v5 | 2.80 | a2c_gae_atari | [a2c_gae_atari_robotank_2026_02_01_132434](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_robotank_2026_02_01_132434) |
-| | 20.07 | ppo_atari | [ppo_atari_robotank_2026_01_06_183413](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_robotank_2026_01_06_183413) |
-| | 9.06 | sac_atari | [sac_atari_robotank_2026_02_12_233446](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_robotank_2026_02_12_233446) |
-| ALE/Seaquest-v5 | 850 | a2c_gae_atari | [a2c_gae_atari_seaquest_2026_02_01_001001](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_seaquest_2026_02_01_001001) |
-| | 1796 | ppo_atari | [ppo_atari_seaquest_2026_01_06_183440](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_seaquest_2026_01_06_183440) |
-| | 2010 | sac_atari | [sac_atari_seaquest_2026_02_13_111005](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_seaquest_2026_02_13_111005) |
-| ALE/Skiing-v5 | -14235 | a2c_gae_atari | [a2c_gae_atari_skiing_2026_02_01_132451](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_skiing_2026_02_01_132451) |
-| | -19340 | ppo_atari | [ppo_atari_skiing_2026_01_06_183424](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_skiing_2026_01_06_183424) |
-| | -16710 | sac_atari | [sac_atari_skiing_2026_02_13_034039](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_skiing_2026_02_13_034039) |
-| ALE/Solaris-v5 | 2224 | a2c_gae_atari | [a2c_gae_atari_solaris_2026_02_01_212137](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_solaris_2026_02_01_212137) |
-| | 2094 | ppo_atari | [ppo_atari_solaris_2026_01_06_192643](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_solaris_2026_01_06_192643) |
-| | 1803 | sac_atari | [sac_atari_solaris_2026_02_13_105520](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_solaris_2026_02_13_105520) |
-| ALE/SpaceInvaders-v5 | 784 | a2c_gae_atari | [a2c_gae_atari_spaceinvaders_2026_02_01_000950](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_spaceinvaders_2026_02_01_000950) |
-| | 726 | ppo_atari | [ppo_atari_spaceinvaders_2026_01_07_102346](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_spaceinvaders_2026_01_07_102346) |
-| | 517 | sac_atari | [sac_atari_spaceinvaders_2026_02_11_080424](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_spaceinvaders_2026_02_11_080424) |
-| ALE/StarGunner-v5 | 8665 | a2c_gae_atari | [a2c_gae_atari_stargunner_2026_02_01_132406](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_stargunner_2026_02_01_132406) |
-| | 47495 | ppo_atari_lam70 | [ppo_atari_lam70_stargunner_2026_01_07_111404](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_stargunner_2026_01_07_111404) |
-| | 3809 | sac_atari | [sac_atari_stargunner_2026_02_13_040158](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_stargunner_2026_02_13_040158) |
-| ALE/Surround-v5 | -9.72 | a2c_gae_atari | [a2c_gae_atari_surround_2026_02_01_132215](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_surround_2026_02_01_132215) |
-| | -2.52 | ppo_atari | [ppo_atari_surround_2026_01_07_102404](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_surround_2026_01_07_102404) |
-| | -9.86 | sac_atari | [sac_atari_surround_2026_02_13_042319](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_surround_2026_02_13_042319) |
-| ALE/Tennis-v5 | -2873 | a2c_gae_atari | [a2c_gae_atari_tennis_2026_02_01_175829](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_tennis_2026_02_01_175829) |
-| | -4.41 | ppo_atari_lam85 | [ppo_atari_lam85_tennis_2026_01_07_223532](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_tennis_2026_01_07_223532) |
-| | -374 | sac_atari | [sac_atari_tennis_2026_02_13_105531](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_tennis_2026_02_13_105531) |
-| ALE/TimePilot-v5 | 3376 | a2c_gae_atari | [a2c_gae_atari_timepilot_2026_02_01_175930](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_timepilot_2026_02_01_175930) |
-| | 4668 | ppo_atari | [ppo_atari_timepilot_2026_01_07_101010](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_timepilot_2026_01_07_101010) |
-| | 3003 | sac_atari | [sac_atari_timepilot_2026_02_13_110656](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_timepilot_2026_02_13_110656) |
-| ALE/Tutankham-v5 | 167 | a2c_gae_atari | [a2c_gae_atari_tutankham_2026_02_01_132347](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_tutankham_2026_02_01_132347) |
-| | 217 | ppo_atari_lam85 | [ppo_atari_lam85_tutankham_2026_01_08_095251](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_tutankham_2026_01_08_095251) |
-| | 126 | sac_atari | [sac_atari_tutankham_2026_02_13_105535](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_tutankham_2026_02_13_105535) |
-| ALE/UpNDown-v5 | 57099 | a2c_gae_atari | [a2c_gae_atari_upndown_2026_02_01_132435](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_upndown_2026_02_01_132435) |
-| | 182472 | ppo_atari | [ppo_atari_upndown_2026_01_07_105708](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_upndown_2026_01_07_105708) |
-| | 3450 | sac_atari | [sac_atari_upndown_2026_02_13_104624](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_upndown_2026_02_13_104624) |
-| ALE/VideoPinball-v5 | 25310 | a2c_gae_atari | [a2c_gae_atari_videopinball_2026_02_01_083457](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_videopinball_2026_02_01_083457) |
-| | 56746 | ppo_atari_lam70 | [ppo_atari_lam70_videopinball_2026_01_07_224359](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_videopinball_2026_01_07_224359) |
-| | 22541 | sac_atari | [sac_atari_videopinball_2026_02_13_222427](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_videopinball_2026_02_13_222427) |
-| ALE/WizardOfWor-v5 | 2682 | a2c_gae_atari | [a2c_gae_atari_wizardofwor_2026_02_01_132449](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_wizardofwor_2026_02_01_132449) |
-| | 5814 | ppo_atari | [ppo_atari_wizardofwor_2026_01_06_221154](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_wizardofwor_2026_01_06_221154) |
-| | 1160 | sac_atari | [sac_atari_wizardofwor_2026_02_13_111635](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_wizardofwor_2026_02_13_111635) |
-| ALE/YarsRevenge-v5 | 24371 | a2c_gae_atari | [a2c_gae_atari_yarsrevenge_2026_02_01_132224](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_yarsrevenge_2026_02_01_132224) |
-| | 17120 | ppo_atari | [ppo_atari_yarsrevenge_2026_01_06_221154](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_yarsrevenge_2026_01_06_221154) |
-| | 13429 | sac_atari | [sac_atari_yarsrevenge_2026_02_13_223033](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_yarsrevenge_2026_02_13_223033) |
-| ALE/Zaxxon-v5 | 29.46 | a2c_gae_atari | [a2c_gae_atari_zaxxon_2026_02_01_131758](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_zaxxon_2026_02_01_131758) |
-| | 10756 | ppo_atari | [ppo_atari_zaxxon_2026_01_06_221154](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_zaxxon_2026_01_06_221154) |
-| | 3453 | sac_atari | [sac_atari_zaxxon_2026_02_13_221310](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_zaxxon_2026_02_13_221310) |
+| ALE/AirRaid-v5 | 7042.84 | ppo_atari_arc | [ppo_atari_arc_airraid_2026_02_13_124015](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_airraid_2026_02_13_124015) |
+| | 1832.54 | sac_atari_arc | [sac_atari_arc_airraid_2026_02_17_104002](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_airraid_2026_02_17_104002) |
+| | 5067 | a2c_gae_atari_arc | [a2c_gae_atari_airraid_2026_02_01_082446](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_airraid_2026_02_01_082446) |
+| ALE/Alien-v5 | 1789.26 | ppo_atari_arc | [ppo_atari_arc_alien_2026_02_13_124017](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_alien_2026_02_13_124017) |
+| | 833.53 | sac_atari_arc | [sac_atari_arc_alien_2026_02_15_200940](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_alien_2026_02_15_200940) |
+| | 1488 | a2c_gae_atari_arc | [a2c_gae_atari_alien_2026_02_01_000858](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_alien_2026_02_01_000858) |
+| ALE/Amidar-v5 | 584.28 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_amidar_2026_02_13_124155](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_amidar_2026_02_13_124155) |
+| | 185.45 | sac_atari_arc | [sac_atari_arc_amidar_2026_02_16_042529](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_amidar_2026_02_16_042529) |
+| | 330 | a2c_gae_atari_arc | [a2c_gae_atari_amidar_2026_02_01_082251](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_amidar_2026_02_01_082251) |
+| ALE/Assault-v5 | 4448.16 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_assault_2026_02_13_124219](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_assault_2026_02_13_124219) |
+| | 1009.42 | sac_atari_arc | [sac_atari_arc_assault_2026_02_16_042532](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_assault_2026_02_16_042532) |
+| | 1646 | a2c_gae_atari_arc | [a2c_gae_atari_assault_2026_02_01_082252](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_assault_2026_02_01_082252) |
+| ALE/Asterix-v5 | 3235.46 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_asterix_2026_02_13_124329](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_asterix_2026_02_13_124329) |
+| | 1504.44 | sac_atari_arc | [sac_atari_arc_asterix_2026_02_16_064430](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_asterix_2026_02_16_064430) |
+| | 2712 | a2c_gae_atari_arc | [a2c_gae_atari_asterix_2026_02_01_082315](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_asterix_2026_02_01_082315) |
+| ALE/Asteroids-v5 | 1577.92 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_asteroids_2026_02_13_171445](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_asteroids_2026_02_13_171445) |
+| | 1203.52 | sac_atari_arc | [sac_atari_arc_asteroids_2026_02_16_051747](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_asteroids_2026_02_16_051747) |
+| | 2106 | a2c_gae_atari_arc | [a2c_gae_atari_asteroids_2026_02_01_082328](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_asteroids_2026_02_01_082328) |
+| ALE/Atlantis-v5 | 848087.19 | ppo_atari_arc | [ppo_atari_arc_atlantis_2026_02_13_171349](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_atlantis_2026_02_13_171349) |
+| | 56787.32 | sac_atari_arc | [sac_atari_arc_atlantis_2026_02_17_105837](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_atlantis_2026_02_17_105837) |
+| | 873365 | a2c_gae_atari_arc | [a2c_gae_atari_atlantis_2026_02_01_082330](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_atlantis_2026_02_01_082330) |
+| ALE/BankHeist-v5 | 1058.25 | ppo_atari_arc | [ppo_atari_arc_bankheist_2026_02_13_230416](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_bankheist_2026_02_13_230416) |
+| | 138.43 | sac_atari_arc | [sac_atari_arc_bankheist_2026_02_17_105306](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_bankheist_2026_02_17_105306) |
+| | 1099 | a2c_gae_atari_arc | [a2c_gae_atari_bankheist_2026_02_01_082403](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_bankheist_2026_02_01_082403) |
+| ALE/BattleZone-v5 | 27176.78 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_battlezone_2026_02_13_171436](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_battlezone_2026_02_13_171436) |
+| | 6906.47 | sac_atari_arc | [sac_atari_arc_battlezone_2026_02_17_112313](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_battlezone_2026_02_17_112313) |
+| | 2437 | a2c_gae_atari_arc | [a2c_gae_atari_battlezone_2026_02_01_082425](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_battlezone_2026_02_01_082425) |
+| ALE/BeamRider-v5 | 2761.75 | ppo_atari_arc | [ppo_atari_arc_beamrider_2026_02_13_171450](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_beamrider_2026_02_13_171450) |
+| | 4061.05 | sac_atari_arc | [sac_atari_arc_beamrider_2026_02_17_110505](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_beamrider_2026_02_17_110505) |
+| | 2767 | a2c_gae_atari_arc | [a2c_gae_atari_beamrider_2026_02_01_000921](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_beamrider_2026_02_01_000921) |
+| ALE/Berzerk-v5 | 835.46 | ppo_atari_arc | [ppo_atari_arc_berzerk_2026_02_13_171449](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_berzerk_2026_02_13_171449) |
+| | 313.87 | sac_atari_arc | [sac_atari_arc_berzerk_2026_02_17_105608](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_berzerk_2026_02_17_105608) |
+| | 439 | a2c_gae_atari_arc | [a2c_gae_atari_berzerk_2026_02_01_082540](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_berzerk_2026_02_01_082540) |
+| ALE/Bowling-v5 | 45.02 | ppo_atari_arc | [ppo_atari_arc_bowling_2026_02_13_230507](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_bowling_2026_02_13_230507) |
+| | 26.55 | sac_atari_arc | [sac_atari_arc_bowling_2026_02_18_101223](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_bowling_2026_02_18_101223) |
+| | 23.96 | a2c_gae_atari_arc | [a2c_gae_atari_bowling_2026_02_01_082529](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_bowling_2026_02_01_082529) |
+| ALE/Boxing-v5 | 92.18 | ppo_atari_arc | [ppo_atari_arc_boxing_2026_02_13_230504](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_boxing_2026_02_13_230504) |
+| | 44.03 | sac_atari_arc | [sac_atari_arc_boxing_2026_02_15_201228](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_boxing_2026_02_15_201228) |
+| | 1.80 | a2c_gae_atari_arc | [a2c_gae_atari_boxing_2026_02_01_082539](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_boxing_2026_02_01_082539) |
+| ALE/Breakout-v5 | 326.47 | ppo_atari_lam70_arc | [ppo_atari_lam70_arc_breakout_2026_02_13_230455](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_arc_breakout_2026_02_13_230455) |
+| | 20.23 | sac_atari_arc | [sac_atari_arc_breakout_2026_02_15_201235](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_breakout_2026_02_15_201235) |
+| | 273 | a2c_gae_atari_arc | [a2c_gae_atari_breakout_2026_01_31_213610](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_breakout_2026_01_31_213610) |
+| ALE/Carnival-v5 | 3912.59 | ppo_atari_lam70_arc | [ppo_atari_lam70_arc_carnival_2026_02_13_230438](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_arc_carnival_2026_02_13_230438) |
+| | 3501.37 | sac_atari_arc | [sac_atari_arc_carnival_2026_02_17_105834](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_carnival_2026_02_17_105834) |
+| | 2170 | a2c_gae_atari_arc | [a2c_gae_atari_carnival_2026_02_01_082726](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_carnival_2026_02_01_082726) |
+| ALE/Centipede-v5 | 4780.75 | ppo_atari_lam70_arc | [ppo_atari_lam70_arc_centipede_2026_02_13_230434](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_arc_centipede_2026_02_13_230434) |
+| | 2255.45 | sac_atari_arc | [sac_atari_arc_centipede_2026_02_18_101425](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_centipede_2026_02_18_101425) |
+| | 1382 | a2c_gae_atari_arc | [a2c_gae_atari_centipede_2026_02_01_082643](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_centipede_2026_02_01_082643) |
+| ALE/ChopperCommand-v5 | 5391.30 | ppo_atari_arc | [ppo_atari_arc_choppercommand_2026_02_13_230448](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_choppercommand_2026_02_13_230448) |
+| | 1036.91 | sac_atari_arc | [sac_atari_arc_choppercommand_2026_02_17_110523](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_choppercommand_2026_02_17_110523) |
+| | 2446 | a2c_gae_atari_arc | [a2c_gae_atari_choppercommand_2026_02_01_082626](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_choppercommand_2026_02_01_082626) |
+| ALE/CrazyClimber-v5 | 112094.03 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_crazyclimber_2026_02_13_230445](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_crazyclimber_2026_02_13_230445) |
+| | 75712.12 | sac_atari_arc | [sac_atari_arc_crazyclimber_2026_02_15_201349](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_crazyclimber_2026_02_15_201349) |
+| | 96943 | a2c_gae_atari_arc | [a2c_gae_atari_crazyclimber_2026_02_01_082625](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_crazyclimber_2026_02_01_082625) |
+| ALE/Defender-v5 | 47894.69 | ppo_atari_lam70_arc | [ppo_atari_lam70_arc_defender_2026_02_14_023317](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_arc_defender_2026_02_14_023317) |
+| | 4386.79 | sac_atari_arc | [sac_atari_arc_defender_2026_02_18_101518](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_defender_2026_02_18_101518) |
+| | 33149 | a2c_gae_atari_arc | [a2c_gae_atari_defender_2026_02_01_082658](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_defender_2026_02_01_082658) |
+| ALE/DemonAttack-v5 | 19370.38 | ppo_atari_lam70_arc | [ppo_atari_lam70_arc_demonattack_2026_02_14_023650](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_arc_demonattack_2026_02_14_023650) |
+| | 4555.58 | sac_atari_arc | [sac_atari_arc_demonattack_2026_02_18_101610](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_demonattack_2026_02_18_101610) |
+| | 2962 | a2c_gae_atari_arc | [a2c_gae_atari_demonattack_2026_02_01_082717](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_demonattack_2026_02_01_082717) |
+| ALE/DoubleDunk-v5 | -3.03 | ppo_atari_arc | [ppo_atari_arc_doubledunk_2026_02_14_043639](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_doubledunk_2026_02_14_043639) |
+| | -18.65 | sac_atari_arc | [sac_atari_arc_doubledunk_2026_02_17_160707](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_doubledunk_2026_02_17_160707) |
+| | -1.69 | a2c_gae_atari_arc | [a2c_gae_atari_doubledunk_2026_02_01_082901](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_doubledunk_2026_02_01_082901) |
+| ALE/Enduro-v5 | 986.46 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_enduro_2026_02_11_101739](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_enduro_2026_02_11_101739) |
+| | 45.80 | sac_atari_arc | [sac_atari_arc_enduro_2026_02_17_160716](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_enduro_2026_02_17_160716) |
+| | 681 | a2c_gae_atari_arc | [a2c_gae_atari_enduro_2026_02_01_001123](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_enduro_2026_02_01_001123) |
+| ALE/FishingDerby-v5 | 25.71 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_fishingderby_2026_02_14_024158](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_fishingderby_2026_02_14_024158) |
+| | -75.82 | sac_atari_arc | [sac_atari_arc_fishingderby_2026_02_17_160848](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_fishingderby_2026_02_17_160848) |
+| | -16.38 | a2c_gae_atari_arc | [a2c_gae_atari_fishingderby_2026_02_01_082906](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_fishingderby_2026_02_01_082906) |
+| ALE/Freeway-v5 | 32.42 | ppo_atari_arc | [ppo_atari_arc_freeway_2026_02_14_023359](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_freeway_2026_02_14_023359) |
+| | 0.00 | sac_atari_arc | [sac_atari_arc_freeway_2026_02_17_161324](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_freeway_2026_02_17_161324) |
+| | 23.13 | a2c_gae_atari_arc | [a2c_gae_atari_freeway_2026_02_01_082931](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_freeway_2026_02_01_082931) |
+| ALE/Frostbite-v5 | 284.07 | ppo_atari_arc | [ppo_atari_arc_frostbite_2026_02_14_024247](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_frostbite_2026_02_14_024247) |
+| | 355.80 | sac_atari_arc | [sac_atari_arc_frostbite_2026_02_17_160759](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_frostbite_2026_02_17_160759) |
+| | 266 | a2c_gae_atari_arc | [a2c_gae_atari_frostbite_2026_02_01_082915](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_frostbite_2026_02_01_082915) |
+| ALE/Gopher-v5 | 6500.38 | ppo_atari_lam70_arc | [ppo_atari_lam70_arc_gopher_2026_02_14_024237](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_arc_gopher_2026_02_14_024237) |
+| | 1608.59 | sac_atari_arc | [sac_atari_arc_gopher_2026_02_17_161047](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_gopher_2026_02_17_161047) |
+| | 984 | a2c_gae_atari_arc | [a2c_gae_atari_gopher_2026_02_01_133323](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_gopher_2026_02_01_133323) |
+| ALE/Gravitar-v5 | 602.58 | ppo_atari_arc | [ppo_atari_arc_gravitar_2026_02_14_075743](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_gravitar_2026_02_14_075743) |
+| | 233.02 | sac_atari_arc | [sac_atari_arc_gravitar_2026_02_17_160858](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_gravitar_2026_02_17_160858) |
+| | 270 | a2c_gae_atari_arc | [a2c_gae_atari_gravitar_2026_02_01_133244](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_gravitar_2026_02_01_133244) |
+| ALE/Hero-v5 | 22477.89 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_hero_2026_02_15_232615](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_hero_2026_02_15_232615) |
+| | 4873.09 | sac_atari_arc | [sac_atari_arc_hero_2026_02_17_161420](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_hero_2026_02_17_161420) |
+| | 18680 | a2c_gae_atari_arc | [a2c_gae_atari_hero_2026_02_01_175903](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_hero_2026_02_01_175903) |
+| ALE/IceHockey-v5 | -4.05 | ppo_atari_arc | [ppo_atari_arc_icehockey_2026_02_14_231829](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_icehockey_2026_02_14_231829) |
+| | -19.78 | sac_atari_arc | [sac_atari_arc_icehockey_2026_02_18_101834](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_icehockey_2026_02_18_101834) |
+| | -5.92 | a2c_gae_atari_arc | [a2c_gae_atari_icehockey_2026_02_01_175745](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_icehockey_2026_02_01_175745) |
+| ALE/Jamesbond-v5 | 710.98 | ppo_atari_arc | [ppo_atari_arc_jamesbond_2026_02_14_080649](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_jamesbond_2026_02_14_080649) |
+| | 328.27 | sac_atari_arc | [sac_atari_arc_jamesbond_2026_02_17_220305](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_jamesbond_2026_02_17_220305) |
+| | 460 | a2c_gae_atari_arc | [a2c_gae_atari_jamesbond_2026_02_01_175945](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_jamesbond_2026_02_01_175945) |
+| ALE/JourneyEscape-v5 | -1248.98 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_journeyescape_2026_02_14_080656](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_journeyescape_2026_02_14_080656) |
+| | -3268.80 | sac_atari_arc | [sac_atari_arc_journeyescape_2026_02_17_215843](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_journeyescape_2026_02_17_215843) |
+| | -965 | a2c_gae_atari_arc | [a2c_gae_atari_journeyescape_2026_02_01_084415](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_journeyescape_2026_02_01_084415) |
+| ALE/Kangaroo-v5 | 10660.35 | ppo_atari_lam70_arc | [ppo_atari_lam70_arc_kangaroo_2026_02_16_030656](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_arc_kangaroo_2026_02_16_030656) |
+| | 2990.74 | sac_atari_arc | [sac_atari_arc_kangaroo_2026_02_17_220652](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_kangaroo_2026_02_17_220652) |
+| | 322 | a2c_gae_atari_arc | [a2c_gae_atari_kangaroo_2026_02_01_084415](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_kangaroo_2026_02_01_084415) |
+| ALE/Krull-v5 | 7874.33 | ppo_atari_arc | [ppo_atari_arc_krull_2026_02_14_080657](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_krull_2026_02_14_080657) |
+| | 6630.02 | sac_atari_arc | [sac_atari_arc_krull_2026_02_17_221656](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_krull_2026_02_17_221656) |
+| | 7519 | a2c_gae_atari_arc | [a2c_gae_atari_krull_2026_02_01_084420](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_krull_2026_02_01_084420) |
+| ALE/KungFuMaster-v5 | 28128.04 | ppo_atari_lam70_arc | [ppo_atari_lam70_arc_kungfumaster_2026_02_14_080730](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_arc_kungfumaster_2026_02_14_080730) |
+| | 9932.72 | sac_atari_arc | [sac_atari_arc_kungfumaster_2026_02_17_221024](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_kungfumaster_2026_02_17_221024) |
+| | 23006 | a2c_gae_atari_arc | [a2c_gae_atari_kungfumaster_2026_02_01_085101](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_kungfumaster_2026_02_01_085101) |
+| ALE/MsPacman-v5 | 2330.74 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_mspacman_2026_02_14_102435](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_mspacman_2026_02_14_102435) |
+| | 1336.96 | sac_atari_arc | [sac_atari_arc_mspacman_2026_02_17_221523](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_mspacman_2026_02_17_221523) |
+| | 2110 | a2c_gae_atari_arc | [a2c_gae_atari_mspacman_2026_02_01_001100](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_mspacman_2026_02_01_001100) |
+| ALE/NameThisGame-v5 | 6879.23 | ppo_atari_arc | [ppo_atari_arc_namethisgame_2026_02_14_103319](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_namethisgame_2026_02_14_103319) |
+| | 3992.71 | sac_atari_arc | [sac_atari_arc_namethisgame_2026_02_17_220905](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_namethisgame_2026_02_17_220905) |
+| | 5412 | a2c_gae_atari_arc | [a2c_gae_atari_namethisgame_2026_02_01_132733](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_namethisgame_2026_02_01_132733) |
+| ALE/Phoenix-v5 | 13923.26 | ppo_atari_lam70_arc | [ppo_atari_lam70_arc_phoenix_2026_02_14_102636](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_arc_phoenix_2026_02_14_102636) |
+| | 3958.46 | sac_atari_arc | [sac_atari_arc_phoenix_2026_02_17_222102](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_phoenix_2026_02_17_222102) |
+| | 5635 | a2c_gae_atari_arc | [a2c_gae_atari_phoenix_2026_02_01_085101](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_phoenix_2026_02_01_085101) |
+| ALE/Pong-v5 | 16.69 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_pong_2026_02_14_103722](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_pong_2026_02_14_103722) |
+| | 10.89 | sac_atari_arc | [sac_atari_arc_pong_2026_02_17_160429](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_pong_2026_02_17_160429) |
+| | 10.17 | a2c_gae_atari_arc | [a2c_gae_atari_pong_2026_01_31_213635](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_pong_2026_01_31_213635) |
+| ALE/Pooyan-v5 | 5308.66 | ppo_atari_lam70_arc | [ppo_atari_lam70_arc_pooyan_2026_02_14_114730](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_arc_pooyan_2026_02_14_114730) |
+| | 2530.78 | sac_atari_arc | [sac_atari_arc_pooyan_2026_02_17_220346](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_pooyan_2026_02_17_220346) |
+| | 2997 | a2c_gae_atari_arc | [a2c_gae_atari_pooyan_2026_02_01_132748](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_pooyan_2026_02_01_132748) |
+| ALE/Qbert-v5 | 15460.48 | ppo_atari_arc | [ppo_atari_arc_qbert_2026_02_14_120409](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_qbert_2026_02_14_120409) |
+| | 3331.98 | sac_atari_arc | [sac_atari_arc_qbert_2026_02_17_223117](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_qbert_2026_02_17_223117) |
+| | 12619 | a2c_gae_atari_arc | [a2c_gae_atari_qbert_2026_01_31_213720](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_qbert_2026_01_31_213720) |
+| ALE/Riverraid-v5 | 9599.75 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_riverraid_2026_02_14_124700](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_riverraid_2026_02_14_124700) |
+| | 4744.95 | sac_atari_arc | [sac_atari_arc_riverraid_2026_02_18_014310](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_riverraid_2026_02_18_014310) |
+| | 6558 | a2c_gae_atari_arc | [a2c_gae_atari_riverraid_2026_02_01_132507](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_riverraid_2026_02_01_132507) |
+| ALE/RoadRunner-v5 | 37980.95 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_roadrunner_2026_02_14_124844](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_roadrunner_2026_02_14_124844) |
+| | 25975.39 | sac_atari_arc | [sac_atari_arc_roadrunner_2026_02_18_015052](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_roadrunner_2026_02_18_015052) |
+| | 29810 | a2c_gae_atari_arc | [a2c_gae_atari_roadrunner_2026_02_01_132509](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_roadrunner_2026_02_01_132509) |
+| ALE/Robotank-v5 | 21.04 | ppo_atari_arc | [ppo_atari_arc_robotank_2026_02_14_124751](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_robotank_2026_02_14_124751) |
+| | 9.01 | sac_atari_arc | [sac_atari_arc_robotank_2026_02_18_032313](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_robotank_2026_02_18_032313) |
+| | 2.80 | a2c_gae_atari_arc | [a2c_gae_atari_robotank_2026_02_01_132434](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_robotank_2026_02_01_132434) |
+| ALE/Seaquest-v5 | 1775.14 | ppo_atari_arc | [ppo_atari_arc_seaquest_2026_02_11_095444](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_seaquest_2026_02_11_095444) |
+| | 1565.44 | sac_atari_arc | [sac_atari_arc_seaquest_2026_02_18_020822](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_seaquest_2026_02_18_020822) |
+| | 850 | a2c_gae_atari_arc | [a2c_gae_atari_seaquest_2026_02_01_001001](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_seaquest_2026_02_01_001001) |
+| ALE/Skiing-v5 | -28217.28 | ppo_atari_arc | [ppo_atari_arc_skiing_2026_02_14_174807](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_skiing_2026_02_14_174807) |
+| | -17464.22 | sac_atari_arc | [sac_atari_arc_skiing_2026_02_18_024444](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_skiing_2026_02_18_024444) |
+| | -14235 | a2c_gae_atari_arc | [a2c_gae_atari_skiing_2026_02_01_132451](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_skiing_2026_02_01_132451) |
+| ALE/Solaris-v5 | 2212.78 | ppo_atari_arc | [ppo_atari_arc_solaris_2026_02_14_124751](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_solaris_2026_02_14_124751) |
+| | 1803.74 | sac_atari_arc | [sac_atari_arc_solaris_2026_02_18_031943](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_solaris_2026_02_18_031943) |
+| | 2224 | a2c_gae_atari_arc | [a2c_gae_atari_solaris_2026_02_01_212137](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_solaris_2026_02_01_212137) |
+| ALE/SpaceInvaders-v5 | 892.49 | ppo_atari_arc | [ppo_atari_arc_spaceinvaders_2026_02_14_131114](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_spaceinvaders_2026_02_14_131114) |
+| | 507.33 | sac_atari_arc | [sac_atari_arc_spaceinvaders_2026_02_18_033139](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_spaceinvaders_2026_02_18_033139) |
+| | 784 | a2c_gae_atari_arc | [a2c_gae_atari_spaceinvaders_2026_02_01_000950](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_spaceinvaders_2026_02_01_000950) |
+| ALE/StarGunner-v5 | 49328.73 | ppo_atari_lam70_arc | [ppo_atari_lam70_arc_stargunner_2026_02_14_131149](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_arc_stargunner_2026_02_14_131149) |
+| | 4295.97 | sac_atari_arc | [sac_atari_arc_stargunner_2026_02_18_033151](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_stargunner_2026_02_18_033151) |
+| | 8665 | a2c_gae_atari_arc | [a2c_gae_atari_stargunner_2026_02_01_132406](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_stargunner_2026_02_01_132406) |
+| ALE/Surround-v5 | -4.47 | ppo_atari_arc | [ppo_atari_arc_surround_2026_02_14_132941](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_surround_2026_02_14_132941) |
+| | -9.87 | sac_atari_arc | [sac_atari_arc_surround_2026_02_18_034423](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_surround_2026_02_18_034423) |
+| | -9.72 | a2c_gae_atari_arc | [a2c_gae_atari_surround_2026_02_01_132215](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_surround_2026_02_01_132215) |
+| ALE/Tennis-v5 | -12.27 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_tennis_2026_02_14_173639](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_tennis_2026_02_14_173639) |
+| | -397.44 | sac_atari_arc | [sac_atari_arc_tennis_2026_02_18_032540](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_tennis_2026_02_18_032540) |
+| | -2873 | a2c_gae_atari_arc | [a2c_gae_atari_tennis_2026_02_01_175829](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_tennis_2026_02_01_175829) |
+| ALE/TimePilot-v5 | 4432.73 | ppo_atari_arc | [ppo_atari_arc_timepilot_2026_02_14_173642](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_timepilot_2026_02_14_173642) |
+| | 3164.97 | sac_atari_arc | [sac_atari_arc_timepilot_2026_02_18_102038](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_timepilot_2026_02_18_102038) |
+| | 3376 | a2c_gae_atari_arc | [a2c_gae_atari_timepilot_2026_02_01_175930](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_timepilot_2026_02_01_175930) |
+| ALE/Tutankham-v5 | 210.87 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_tutankham_2026_02_14_173722](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_tutankham_2026_02_14_173722) |
+| | 147.25 | sac_atari_arc | [sac_atari_arc_tutankham_2026_02_18_102729](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_tutankham_2026_02_18_102729) |
+| | 167 | a2c_gae_atari_arc | [a2c_gae_atari_tutankham_2026_02_01_132347](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_tutankham_2026_02_01_132347) |
+| ALE/UpNDown-v5 | 147168.80 | ppo_atari_lam85_arc | [ppo_atari_lam85_arc_upndown_2026_02_15_232448](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam85_arc_upndown_2026_02_15_232448) |
+| | 3351.89 | sac_atari_arc | [sac_atari_arc_upndown_2026_02_18_135442](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_upndown_2026_02_18_135442) |
+| | 57099 | a2c_gae_atari_arc | [a2c_gae_atari_upndown_2026_02_01_132435](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_upndown_2026_02_01_132435) |
+| ALE/VideoPinball-v5 | 38370.30 | ppo_atari_lam70_arc | [ppo_atari_lam70_arc_videopinball_2026_02_14_173728](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_lam70_arc_videopinball_2026_02_14_173728) |
+| | 21088.68 | sac_atari_arc | [sac_atari_arc_videopinball_2026_02_18_141245](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_videopinball_2026_02_18_141245) |
+| | 25310 | a2c_gae_atari_arc | [a2c_gae_atari_videopinball_2026_02_01_083457](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_videopinball_2026_02_01_083457) |
+| ALE/WizardOfWor-v5 | 6100.42 | ppo_atari_arc | [ppo_atari_arc_wizardofwor_2026_02_14_173945](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_wizardofwor_2026_02_14_173945) |
+| | 1241.92 | sac_atari_arc | [sac_atari_arc_wizardofwor_2026_02_18_140750](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_wizardofwor_2026_02_18_140750) |
+| | 2682 | a2c_gae_atari_arc | [a2c_gae_atari_wizardofwor_2026_02_01_132449](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_wizardofwor_2026_02_01_132449) |
+| ALE/YarsRevenge-v5 | 12873.91 | ppo_atari_arc | [ppo_atari_arc_yarsrevenge_2026_02_14_174019](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_yarsrevenge_2026_02_14_174019) |
+| | 13710.18 | sac_atari_arc | [sac_atari_arc_yarsrevenge_2026_02_18_134921](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_yarsrevenge_2026_02_18_134921) |
+| | 24371 | a2c_gae_atari_arc | [a2c_gae_atari_yarsrevenge_2026_02_01_132224](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_yarsrevenge_2026_02_01_132224) |
+| ALE/Zaxxon-v5 | 9523.49 | ppo_atari_arc | [ppo_atari_arc_zaxxon_2026_02_14_174806](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/ppo_atari_arc_zaxxon_2026_02_14_174806) |
+| | 3205.98 | sac_atari_arc | [sac_atari_arc_zaxxon_2026_02_18_135502](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/sac_atari_arc_zaxxon_2026_02_18_135502) |
+| | 29.46 | a2c_gae_atari_arc | [a2c_gae_atari_zaxxon_2026_02_01_131758](https://huggingface.co/datasets/SLM-Lab/benchmark/tree/main/data/a2c_gae_atari_zaxxon_2026_02_01_131758) |
+
 
 **Training Curves** (A2C vs PPO vs SAC):
 
 | | | |
 |:---:|:---:|:---:|
-| ![AirRaid](./plots/AirRaid_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Alien](./plots/Alien_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Amidar](./plots/Amidar_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![Assault](./plots/Assault_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Asterix](./plots/Asterix_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Asteroids](./plots/Asteroids_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![Atlantis](./plots/Atlantis_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![BankHeist](./plots/BankHeist_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![BattleZone](./plots/BattleZone_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![BeamRider](./plots/BeamRider_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Berzerk](./plots/Berzerk_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Bowling](./plots/Bowling_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![Boxing](./plots/Boxing_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Breakout](./plots/Breakout_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Carnival](./plots/Carnival_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![Centipede](./plots/Centipede_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![ChopperCommand](./plots/ChopperCommand_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![CrazyClimber](./plots/CrazyClimber_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![Defender](./plots/Defender_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![DemonAttack](./plots/DemonAttack_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![DoubleDunk](./plots/DoubleDunk_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![ElevatorAction](./plots/ElevatorAction_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Enduro](./plots/Enduro_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![FishingDerby](./plots/FishingDerby_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![Freeway](./plots/Freeway_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Frostbite](./plots/Frostbite_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Gopher](./plots/Gopher_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![Gravitar](./plots/Gravitar_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Hero](./plots/Hero_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![IceHockey](./plots/IceHockey_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![Jamesbond](./plots/Jamesbond_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![JourneyEscape](./plots/JourneyEscape_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Kangaroo](./plots/Kangaroo_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![Krull](./plots/Krull_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![KungFuMaster](./plots/KungFuMaster_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![MsPacman](./plots/MsPacman_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![NameThisGame](./plots/NameThisGame_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Phoenix](./plots/Phoenix_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Pong](./plots/Pong_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![Pooyan](./plots/Pooyan_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Qbert](./plots/Qbert_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Riverraid](./plots/Riverraid_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![RoadRunner](./plots/RoadRunner_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Robotank](./plots/Robotank_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Seaquest](./plots/Seaquest_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![Skiing](./plots/Skiing_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Solaris](./plots/Solaris_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![SpaceInvaders](./plots/SpaceInvaders_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![StarGunner](./plots/StarGunner_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Surround](./plots/Surround_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Tennis](./plots/Tennis_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![TimePilot](./plots/TimePilot_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Tutankham](./plots/Tutankham_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![UpNDown](./plots/UpNDown_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![VideoPinball](./plots/VideoPinball_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![WizardOfWor](./plots/WizardOfWor_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![YarsRevenge](./plots/YarsRevenge_multi_trial_graph_mean_returns_ma_vs_frames.png) |
-| ![Zaxxon](./plots/Zaxxon_multi_trial_graph_mean_returns_ma_vs_frames.png) | | |
-
-**Skipped** (hard exploration): Adventure, MontezumaRevenge, Pitfall, PrivateEye, Venture
+| ![AirRaid](plots/AirRaid-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Alien](plots/Alien-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Amidar](plots/Amidar-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Assault](plots/Assault-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Asterix](plots/Asterix-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Asteroids](plots/Asteroids-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Atlantis](plots/Atlantis-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![BankHeist](plots/BankHeist-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![BattleZone](plots/BattleZone-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![BeamRider](plots/BeamRider-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Berzerk](plots/Berzerk-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Bowling](plots/Bowling-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Boxing](plots/Boxing-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Breakout](plots/Breakout-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Carnival](plots/Carnival-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Centipede](plots/Centipede-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![ChopperCommand](plots/ChopperCommand-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![CrazyClimber](plots/CrazyClimber-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Defender](plots/Defender-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![DemonAttack](plots/DemonAttack-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![DoubleDunk](plots/DoubleDunk-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Enduro](plots/Enduro-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![FishingDerby](plots/FishingDerby-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Freeway](plots/Freeway-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Frostbite](plots/Frostbite-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Gopher](plots/Gopher-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Gravitar](plots/Gravitar-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Hero](plots/Hero-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![IceHockey](plots/IceHockey-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Jamesbond](plots/Jamesbond-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![JourneyEscape](plots/JourneyEscape-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Kangaroo](plots/Kangaroo-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Krull](plots/Krull-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![KungFuMaster](plots/KungFuMaster-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![MsPacman](plots/MsPacman-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![NameThisGame](plots/NameThisGame-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Phoenix](plots/Phoenix-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Pong](plots/Pong-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Pooyan](plots/Pooyan-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Qbert](plots/Qbert-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Riverraid](plots/Riverraid-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![RoadRunner](plots/RoadRunner-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Robotank](plots/Robotank-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Seaquest](plots/Seaquest-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Skiing](plots/Skiing-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Solaris](plots/Solaris-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![SpaceInvaders](plots/SpaceInvaders-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![StarGunner](plots/StarGunner-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Surround](plots/Surround-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Tennis](plots/Tennis-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![TimePilot](plots/TimePilot-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![Tutankham](plots/Tutankham-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![UpNDown](plots/UpNDown-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![VideoPinball](plots/VideoPinball-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+| ![WizardOfWor](plots/WizardOfWor-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![YarsRevenge](plots/YarsRevenge-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) | ![Zaxxon](plots/Zaxxon-v5_multi_trial_graph_mean_returns_ma_vs_frames.png) |
+
+**Skipped**: Adventure, MontezumaRevenge, Pitfall, PrivateEye, Venture (hard exploration), ElevatorAction (deprecated env)
 
 <details>
 <summary><b>PPO Lambda Comparison</b> (click to expand)</summary>
 
-| ENV | ppo_atari | ppo_atari_lam85 | ppo_atari_lam70 |
-|-----|-----------|-----------------|-----------------|
-| ALE/AirRaid-v5 | **8245** | - | - |
-| ALE/Alien-v5 | **1453** | 1353 | 1274 |
-| ALE/Amidar-v5 | 574 | **580** | - |
-| ALE/Assault-v5 | 4059 | **4293** | 3314 |
-| ALE/Asterix-v5 | 2967 | **3482** | - |
-| ALE/Asteroids-v5 | 1497 | **1554** | - |
-| ALE/Atlantis-v5 | **792886** | 754k | 710k |
-| ALE/BankHeist-v5 | **1045** | 1045 | - |
-| ALE/BattleZone-v5 | 21270 | **26383** | 13857 |
-| ALE/BeamRider-v5 | **2765** | - | - |
-| ALE/Berzerk-v5 | **1072** | - | - |
-| ALE/Bowling-v5 | **46.45** | - | - |
-| ALE/Boxing-v5 | **91.17** | - | - |
-| ALE/Breakout-v5 | 191 | 292 | **327** |
-| ALE/Carnival-v5 | 3071 | 3013 | **3967** |
-| ALE/Centipede-v5 | 3917 | - | **4915** |
-| ALE/ChopperCommand-v5 | **5355** | - | - |
-| ALE/CrazyClimber-v5 | 107183 | **107370** | - |
-| ALE/Defender-v5 | 37162 | - | **51439** |
-| ALE/DemonAttack-v5 | 7755 | - | **16558** |
-| ALE/DoubleDunk-v5 | **-2.38** | - | - |
-| ALE/ElevatorAction-v5 | **5446** | 363 | 3933 |
-| ALE/Enduro-v5 | 414 | **898** | 872 |
-| ALE/FishingDerby-v5 | 22.80 | **27.10** | - |
-| ALE/Freeway-v5 | **31.30** | - | - |
-| ALE/Frostbite-v5 | **301** | 275 | 267 |
-| ALE/Gopher-v5 | 4172 | - | **6508** |
-| ALE/Gravitar-v5 | **599** | 253 | 145 |
-| ALE/Hero-v5 | 21052 | **28238** | - |
-| ALE/IceHockey-v5 | **-3.93** | -5.58 | -7.36 |
-| ALE/Jamesbond-v5 | **662** | - | - |
-| ALE/JourneyEscape-v5 | -1582 | **-1252** | -1547 |
-| ALE/Kangaroo-v5 | 2623 | **9912** | - |
-| ALE/Krull-v5 | **7841** | - | - |
-| ALE/KungFuMaster-v5 | 18973 | 28334 | **29068** |
-| ALE/MsPacman-v5 | 2308 | **2372** | 2297 |
-| ALE/NameThisGame-v5 | **5993** | - | - |
-| ALE/Phoenix-v5 | 7940 | - | **15659** |
-| ALE/Pong-v5 | 15.01 | **16.91** | 12.85 |
-| ALE/Pooyan-v5 | 4704 | - | **5716** |
-| ALE/Qbert-v5 | **15094** | - | - |
-| ALE/Riverraid-v5 | 7319 | **9428** | - |
-| ALE/RoadRunner-v5 | 24204 | **37015** | - |
-| ALE/Robotank-v5 | **20.07** | 8.24 | 2.59 |
-| ALE/Seaquest-v5 | 1796 | - | **2010** |
-| ALE/Skiing-v5 | **-19340** | -22980 | -29975 |
-| ALE/Solaris-v5 | **2094** | 1803 | - |
-| ALE/SpaceInvaders-v5 | **726** | - | - |
-| ALE/StarGunner-v5 | 31862 | - | **47495** |
-| ALE/Surround-v5 | **-2.52** | - | -6.79 |
-| ALE/Tennis-v5 | -7.66 | **-4.41** | -374 |
-| ALE/TimePilot-v5 | **4668** | 3003 | - |
-| ALE/Tutankham-v5 | 203 | **217** | 126 |
-| ALE/UpNDown-v5 | **182472** | 3450 | - |
-| ALE/VideoPinball-v5 | 31385 | 22541 | **56746** |
-| ALE/WizardOfWor-v5 | **5814** | 1160 | 4740 |
-| ALE/YarsRevenge-v5 | **17120** | 13429 | - |
-| ALE/Zaxxon-v5 | **10756** | 3453 | - |
+| ENV | ppo_atari_arc | ppo_atari_lam85_arc | ppo_atari_lam70_arc |
+|-----|---------------|---------------------|---------------------|
+| ALE/AirRaid-v5 | **7042.84** | - | - |
+| ALE/Alien-v5 | **1789.26** | - | - |
+| ALE/Amidar-v5 | - | **584.28** | - |
+| ALE/Assault-v5 | - | **4448.16** | - |
+| ALE/Asterix-v5 | - | **3235.46** | - |
+| ALE/Asteroids-v5 | - | **1577.92** | - |
+| ALE/Atlantis-v5 | **848087.19** | - | - |
+| ALE/BankHeist-v5 | **1058.25** | - | - |
+| ALE/BattleZone-v5 | - | **27176.78** | - |
+| ALE/BeamRider-v5 | **2761.75** | - | - |
+| ALE/Berzerk-v5 | **835.46** | - | - |
+| ALE/Bowling-v5 | **45.02** | - | - |
+| ALE/Boxing-v5 | **92.18** | - | - |
+| ALE/Breakout-v5 | - | - | **326.47** |
+| ALE/Carnival-v5 | - | - | **3912.59** |
+| ALE/Centipede-v5 | - | - | **4780.75** |
+| ALE/ChopperCommand-v5 | **5391.30** | - | - |
+| ALE/CrazyClimber-v5 | - | **112094.03** | - |
+| ALE/Defender-v5 | - | - | **47894.69** |
+| ALE/DemonAttack-v5 | - | - | **19370.38** |
+| ALE/DoubleDunk-v5 | **-3.03** | - | - |
+| ALE/Enduro-v5 | - | **986.46** | - |
+| ALE/FishingDerby-v5 | - | **25.71** | - |
+| ALE/Freeway-v5 | **32.42** | - | - |
+| ALE/Frostbite-v5 | **284.07** | - | - |
+| ALE/Gopher-v5 | - | - | **6500.38** |
+| ALE/Gravitar-v5 | **602.58** | - | - |
+| ALE/Hero-v5 | - | **22477.89** | - |
+| ALE/IceHockey-v5 | **-4.05** | - | - |
+| ALE/Jamesbond-v5 | **710.98** | - | - |
+| ALE/JourneyEscape-v5 | - | **-1248.98** | - |
+| ALE/Kangaroo-v5 | - | - | **10660.35** |
+| ALE/Krull-v5 | **7874.33** | - | - |
+| ALE/KungFuMaster-v5 | - | - | **28128.04** |
+| ALE/MsPacman-v5 | - | **2330.74** | - |
+| ALE/NameThisGame-v5 | **6879.23** | - | - |
+| ALE/Phoenix-v5 | - | - | **13923.26** |
+| ALE/Pong-v5 | - | **16.69** | - |
+| ALE/Pooyan-v5 | - | - | **5308.66** |
+| ALE/Qbert-v5 | **15460.48** | - | - |
+| ALE/Riverraid-v5 | - | **9599.75** | - |
+| ALE/RoadRunner-v5 | - | **37980.95** | - |
+| ALE/Robotank-v5 | **21.04** | - | - |
+| ALE/Seaquest-v5 | **1775.14** | - | - |
+| ALE/Skiing-v5 | **-28217.28** | - | - |
+| ALE/Solaris-v5 | **2212.78** | - | - |
+| ALE/SpaceInvaders-v5 | **892.49** | - | - |
+| ALE/StarGunner-v5 | - | - | **49328.73** |
+| ALE/Surround-v5 | **-4.47** | - | - |
+| ALE/Tennis-v5 | - | **-12.27** | - |
+| ALE/TimePilot-v5 | **4432.73** | - | - |
+| ALE/Tutankham-v5 | - | **210.87** | - |
+| ALE/UpNDown-v5 | - | **147168.80** | - |
+| ALE/VideoPinball-v5 | - | - | **38370.30** |
+| ALE/WizardOfWor-v5 | **6100.42** | - | - |
+| ALE/YarsRevenge-v5 | **12873.91** | - | - |
+| ALE/Zaxxon-v5 | **9523.49** | - | - |
 
 **Legend**: **Bold** = Best score | - = Not tested
 
 </details>
 
----
\ No newline at end of file
diff --git a/CHANGELOG.md b/docs/CHANGELOG.md
similarity index 94%
rename from CHANGELOG.md
rename to docs/CHANGELOG.md
index a7c1a80de..3950639cc 100644
--- a/CHANGELOG.md
+++ b/docs/CHANGELOG.md
@@ -1,3 +1,14 @@
+# SLM-Lab v5.1.0
+
+TorchArc YAML benchmarks replace original hardcoded network architectures across all benchmark categories.
+
+- **TorchArc integration**: All algorithms (REINFORCE, SARSA, DQN, DDQN+PER, A2C, PPO, SAC) now use TorchArc YAML-defined networks instead of hardcoded PyTorch modules
+- **Full benchmark validation**: Classic Control, Box2D, MuJoCo (11 envs), and Atari (54 games) re-benchmarked with TorchArc — results match or exceed original scores
+- **SAC Atari**: New SAC Atari benchmarks (48 games) with discrete action support
+- **Pre-commit hooks**: Conventional commit message validation via `.githooks/commit-msg`
+
+---
+
 # SLM-Lab v5.0.0
 
 Modernization release for the current RL ecosystem. Updates SLM-Lab from OpenAI Gym to Gymnasium, adds correct handling of episode termination (the `terminated`/`truncated` fix), and migrates to modern Python tooling.
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diff --git a/docs/plots/YarsRevenge-v5_multi_trial_graph_mean_returns_ma_vs_frames.png b/docs/plots/YarsRevenge-v5_multi_trial_graph_mean_returns_ma_vs_frames.png
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diff --git a/docs/plots/Zaxxon-v5_multi_trial_graph_mean_returns_ma_vs_frames.png b/docs/plots/Zaxxon-v5_multi_trial_graph_mean_returns_ma_vs_frames.png
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diff --git a/pyproject.toml b/pyproject.toml
index 313713c02..5aa54b510 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "slm-lab"
-version = "5.0.2"
+version = "5.1.0"
 description = "Modular Deep Reinforcement Learning framework in PyTorch."
 readme = "README.md"
 requires-python = ">=3.12.0"
@@ -24,7 +24,6 @@ classifiers = [
 # Core dependencies - CLI orchestration, plotting, HF sync (no heavy ML deps)
 dependencies = [
     "colorlover>=0.3.0",
-    "dstack>=0.18.0",
     "huggingface-hub>=0.34.4",
     "kaleido==0.2.0",
     "loguru>=0.7.3",
@@ -55,6 +54,7 @@ ml = [
     "ray[default,tune]>=2.44.0",
     "tensorboard>=2.19.0",
     "torch>=2.8.0",
+    "torcharc>=1.0.0",
 ]
 # Dev dependencies - install with: uv sync --group dev
 dev = [
@@ -73,6 +73,11 @@ dev = [
 # Include ML deps by default for local training
 # Use `uv sync --no-default-groups` for minimal CLI-only install
 default-groups = ["ml"]
+# Restrict resolution to platforms we actually use (macOS dev + Linux training)
+environments = [
+    "sys_platform == 'darwin'",
+    "sys_platform == 'linux' and platform_machine == 'x86_64'",
+]
 
 [[tool.uv.index]]
 name = "pytorch-cu128"
diff --git a/slm_lab/agent/memory/replay.py b/slm_lab/agent/memory/replay.py
index a1a3be79a..3b7192bfa 100644
--- a/slm_lab/agent/memory/replay.py
+++ b/slm_lab/agent/memory/replay.py
@@ -152,8 +152,8 @@ def add_experience(
         """Implementation for update() to add experience to memory, expanding the memory size if necessary"""
         # Move head pointer. Wrap around if necessary
         self.head = (self.head + 1) % self.max_size
-        # Use uint8 for image observations (ndim >= 3) to save memory; float16 for vectors
-        state_dtype = np.uint8 if state.ndim >= 3 else np.float16
+        # Preserve dtype: uint8 images stay uint8 (memory efficient); everything else float16
+        state_dtype = np.uint8 if state.dtype == np.uint8 else np.float16
         self.states[self.head] = state.astype(state_dtype)
         self.actions[self.head] = action
         self.rewards[self.head] = reward
diff --git a/slm_lab/agent/net/__init__.py b/slm_lab/agent/net/__init__.py
index 5290ec8cd..6278cd1a3 100644
--- a/slm_lab/agent/net/__init__.py
+++ b/slm_lab/agent/net/__init__.py
@@ -3,3 +3,9 @@
 from slm_lab.agent.net.conv import *
 from slm_lab.agent.net.mlp import *
 from slm_lab.agent.net.recurrent import *
+
+# Optional: torcharc-based networks (requires torcharc package)
+try:
+    from slm_lab.agent.net.torcharc_net import *
+except ImportError:
+    pass
diff --git a/slm_lab/agent/net/torcharc_net.py b/slm_lab/agent/net/torcharc_net.py
new file mode 100644
index 000000000..bf1a744d6
--- /dev/null
+++ b/slm_lab/agent/net/torcharc_net.py
@@ -0,0 +1,145 @@
+import numpy as np
+import torcharc
+import torch
+import torch.nn as nn
+
+from slm_lab.agent.net import net_util
+from slm_lab.agent.net.base import Net
+from slm_lab.lib import util
+
+
+class TorchArcNet(Net, nn.Module):
+    """Network built from torcharc YAML architecture specs.
+
+    The torcharc spec defines the body/feature extractor.
+    Output tails are added automatically based on out_dim (same as MLPNet).
+
+    net_spec example:
+        net:
+          type: TorchArcNet
+          arc:  # inline torcharc spec
+            modules:
+              body:
+                Sequential:
+                  - LazyLinear:
+                      out_features: 64
+                  - Tanh:
+                  - LazyLinear:
+                      out_features: 64
+                  - Tanh:
+            graph:
+              input: x
+              modules:
+                body: [x]
+              output: body
+          # Standard SLM-Lab params
+          clip_grad_val: 0.5
+          loss_spec: {name: MSELoss}
+          optim_spec: {name: Adam, lr: 0.001}
+          gpu: auto
+    """
+
+    def __init__(self, net_spec, in_dim, out_dim):
+        nn.Module.__init__(self)
+        super().__init__(net_spec, in_dim, out_dim)
+        # set defaults (same pattern as MLPNet)
+        util.set_attr(
+            self,
+            dict(
+                out_layer_activation=None,
+                init_fn=None,
+                shared=False,
+                normalize=False,
+                hid_layers_activation="relu",
+                clip_grad_val=None,
+                loss_spec={"name": "MSELoss"},
+                optim_spec={"name": "Adam"},
+                lr_scheduler_spec=None,
+                update_type="replace",
+                update_frequency=1,
+                polyak_coef=0.0,
+                gpu=False,
+                log_std_init=None,
+                actor_init_std=None,
+                critic_init_std=None,
+            ),
+        )
+        util.set_attr(
+            self,
+            self.net_spec,
+            [
+                "out_layer_activation",
+                "init_fn",
+                "shared",
+                "normalize",
+                "hid_layers_activation",
+                "clip_grad_val",
+                "loss_spec",
+                "optim_spec",
+                "lr_scheduler_spec",
+                "update_type",
+                "update_frequency",
+                "polyak_coef",
+                "gpu",
+                "log_std_init",
+                "actor_init_std",
+                "critic_init_std",
+            ],
+        )
+
+        # Build body from torcharc spec (inline dict or file path)
+        arc_spec = net_spec.get("arc")
+        arc_file = net_spec.get("arc_file")
+        if arc_spec:
+            self.body = torcharc.build(arc_spec)
+        elif arc_file:
+            self.body = torcharc.build(arc_file)
+        else:
+            raise ValueError(
+                "TorchArcNet requires 'arc' (inline spec) or 'arc_file' (path) in net_spec"
+            )
+
+        # Move body to device before dummy forward so LazyLinear initializes on correct device
+        self.body.to(self.device)
+        # Compute body output dim via dummy forward pass (triggers LazyLinear init on device)
+        body_out_dim = self._get_body_out_dim()
+
+        # Build output tails (same as MLPNet)
+        self.tails, self.log_std = net_util.build_tails(
+            body_out_dim, self.out_dim, self.out_layer_activation, self.log_std_init
+        )
+
+        net_util.init_layers(self, self.init_fn)
+        net_util.init_tails(self, self.actor_init_std, self.critic_init_std)
+        self.loss_fn = net_util.get_loss_fn(self, self.loss_spec)
+        self.to(self.device)
+        self.train()
+
+    def _get_body_out_dim(self):
+        """Compute body output dimension via dummy forward pass."""
+        with torch.no_grad():
+            if isinstance(self.in_dim, (int, np.integer)):
+                dummy = torch.ones(1, self.in_dim, device=self.device)
+            elif isinstance(self.in_dim, (list, tuple)):
+                dummy = torch.ones(1, *self.in_dim, device=self.device)
+            else:
+                dummy = torch.ones(1, self.in_dim, device=self.device)
+            out = self.body(dummy)
+            if isinstance(out, (list, tuple)):
+                out = out[0]
+            # Flatten if needed (for conv outputs)
+            if out.dim() > 2:
+                return out.view(out.size(0), -1).shape[-1]
+            return out.shape[-1]
+
+    def forward(self, x):
+        """Forward pass: body -> tails"""
+        if self.normalize:
+            x = x / 255.0
+        x = self.body(x)
+        if isinstance(x, (list, tuple)):
+            x = x[0]
+        # Flatten if needed (for conv body outputs)
+        if x.dim() > 2:
+            x = x.view(x.size(0), -1)
+        return net_util.forward_tails(x, self.tails, self.log_std)
diff --git a/slm_lab/cli/plot.py b/slm_lab/cli/plot.py
index 0cc177171..902dacec8 100644
--- a/slm_lab/cli/plot.py
+++ b/slm_lab/cli/plot.py
@@ -192,8 +192,9 @@ def plot(
         if color is None:
             palette[i] = default_palette[i % len(default_palette)]
 
-    # Generate output filename from title
-    safe_title = final_title.replace(' ', '_').replace('(', '').replace(')', '')
+    # Generate output filename from title (strips ALE/ prefix for Atari env names)
+    filename_title = Path(final_title).name  # ALE/Pong-v5 → Pong-v5
+    safe_title = filename_title.replace(' ', '_').replace('(', '').replace(')', '')
     graph_prepath = str(output_path / safe_title)
 
     viz.plot_multi_trial(
diff --git a/slm_lab/cli/remote.py b/slm_lab/cli/remote.py
index d63979cce..6049993cd 100644
--- a/slm_lab/cli/remote.py
+++ b/slm_lab/cli/remote.py
@@ -49,7 +49,7 @@ def run_remote(
     config_mode = "search" if mode == "search" else "train"
     config_file = f".dstack/run-{hw}-{config_mode}.yml"
 
-    cmd = ["uv", "run", "--no-default-groups", "dstack", "apply", "-f", config_file, "-y", "--detach", "--name", run_name]
+    cmd = ["dstack", "apply", "-f", config_file, "-y", "--detach", "--name", run_name]
     env = os.environ.copy()
     env["SPEC_FILE"] = spec_file
     env["SPEC_NAME"] = spec_name
diff --git a/slm_lab/env/__init__.py b/slm_lab/env/__init__.py
index a76ef5b0a..e0cf8568a 100644
--- a/slm_lab/env/__init__.py
+++ b/slm_lab/env/__init__.py
@@ -39,6 +39,7 @@
 # Register ALE environments on import
 try:
     import ale_py
+
     os.environ.setdefault("ALE_PY_SILENCE", "1")
     gym.register_envs(ale_py)
 except ImportError:
@@ -47,21 +48,32 @@
 logger = logger.get_logger(__name__)
 
 # Keys handled by make_env, not passed to gym.make
-RESERVED_KEYS = {"name", "num_envs", "max_t", "max_frame", "normalize_obs", "normalize_reward", "clip_obs", "clip_reward"}
+RESERVED_KEYS = {
+    "name",
+    "num_envs",
+    "max_t",
+    "max_frame",
+    "normalize_obs",
+    "normalize_reward",
+    "clip_obs",
+    "clip_reward",
+}
 
 
 def _needs_action_rescaling(env: gym.Env) -> bool:
     """Check if action space needs rescaling to [-1, 1]."""
-    action_space = getattr(env, 'single_action_space', env.action_space)
+    action_space = getattr(env, "single_action_space", env.action_space)
     if not isinstance(action_space, spaces.Box):
         return False
     return not (
-        np.allclose(action_space.low, -1.0, atol=1e-6) and
-        np.allclose(action_space.high, 1.0, atol=1e-6)
+        np.allclose(action_space.low, -1.0, atol=1e-6)
+        and np.allclose(action_space.high, 1.0, atol=1e-6)
     )
 
 
-def _get_vectorization_mode(name: str, num_envs: int, is_rendering: bool = False) -> str:
+def _get_vectorization_mode(
+    name: str, num_envs: int, is_rendering: bool = False
+) -> str:
     """Select vectorization mode based on environment type."""
     entry_point = gym.envs.registry[name].entry_point.lower()
 
@@ -98,7 +110,9 @@ def _set_env_attributes(env: gym.Env, spec: dict[str, Any]) -> None:
     # State dimension
     obs_space = env.observation_space
     if isinstance(obs_space, spaces.Box):
-        env.state_dim = obs_space.shape[0] if len(obs_space.shape) == 1 else obs_space.shape
+        env.state_dim = (
+            obs_space.shape[0] if len(obs_space.shape) == 1 else obs_space.shape
+        )
     else:
         env.state_dim = getattr(obs_space, "n", obs_space.shape)
 
@@ -109,7 +123,11 @@ def _set_env_attributes(env: gym.Env, spec: dict[str, Any]) -> None:
         env.is_discrete = True
         env.is_multi = False
     elif isinstance(action_space, spaces.Box):
-        env.action_dim = action_space.shape[0] if len(action_space.shape) == 1 else action_space.shape
+        env.action_dim = (
+            action_space.shape[0]
+            if len(action_space.shape) == 1
+            else action_space.shape
+        )
         env.is_discrete = False
         env.is_multi = len(action_space.shape) > 1 or action_space.shape[0] > 1
     elif isinstance(action_space, spaces.MultiDiscrete):
@@ -124,7 +142,9 @@ def _set_env_attributes(env: gym.Env, spec: dict[str, Any]) -> None:
         raise NotImplementedError(f"Action space {type(action_space)} not supported")
 
     # Timing config
-    env.max_t = env_spec.get("max_t") or getattr(env.spec, "max_episode_steps", None) or 108000
+    env.max_t = (
+        env_spec.get("max_t") or getattr(env.spec, "max_episode_steps", None) or 108000
+    )
     if spec["meta"]["distributed"] is not False:
         env.max_frame = int(env.max_frame / spec["meta"]["max_session"])
     env.done = False
@@ -153,20 +173,48 @@ def make_env(spec: dict[str, Any]) -> gym.Env:
     gamma = spec.get("agent", {}).get("algorithm", {}).get("gamma", 0.99)
 
     if num_envs > 1:
-        env = _make_vector_env(name, num_envs, is_atari, render_mode, make_kwargs,
-                               normalize_obs, normalize_reward, clip_obs, clip_reward, gamma)
+        env = _make_vector_env(
+            name,
+            num_envs,
+            is_atari,
+            render_mode,
+            make_kwargs,
+            normalize_obs,
+            normalize_reward,
+            clip_obs,
+            clip_reward,
+            gamma,
+        )
     else:
-        env = _make_single_env(name, is_atari, render_mode, make_kwargs,
-                               normalize_obs, normalize_reward, clip_obs, clip_reward, gamma)
+        env = _make_single_env(
+            name,
+            is_atari,
+            render_mode,
+            make_kwargs,
+            normalize_obs,
+            normalize_reward,
+            clip_obs,
+            clip_reward,
+            gamma,
+        )
 
     _set_env_attributes(env, spec)
     ClockWrapperClass = VectorClockWrapper if env.is_venv else ClockWrapper
     return ClockWrapperClass(env, env.max_frame)
 
 
-def _make_vector_env(name: str, num_envs: int, is_atari: bool, render_mode: str | None,
-                     make_kwargs: dict, normalize_obs: bool, normalize_reward: bool,
-                     clip_obs: float | None, clip_reward: float | None, gamma: float) -> gym.Env:
+def _make_vector_env(
+    name: str,
+    num_envs: int,
+    is_atari: bool,
+    render_mode: str | None,
+    make_kwargs: dict,
+    normalize_obs: bool,
+    normalize_reward: bool,
+    clip_obs: float | None,
+    clip_reward: float | None,
+    gamma: float,
+) -> gym.Env:
     """Create vector environment."""
     is_rendering = bool(render_mode)
     vectorization_mode = _get_vectorization_mode(name, num_envs, is_rendering)
@@ -182,21 +230,32 @@ def _make_vector_env(name: str, num_envs: int, is_atari: bool, render_mode: str
             make_kwargs.pop("life_loss_info", None)
             make_kwargs.pop("reward_clipping", None)
             make_kwargs["render_mode"] = "rgb_array"
+
             def preprocess(env):
                 return FrameStackObservation(
-                    AtariPreprocessing(env, frame_skip=1), stack_size=4, padding_type="zero"
+                    AtariPreprocessing(env, frame_skip=1, scale_obs=False),
+                    stack_size=4,
+                    padding_type="zero",
                 )
+
             per_env_wrappers = [preprocess]
             logger.info(f"Atari sync: {num_envs} envs with preprocessing wrappers")
     else:
         make_kwargs["render_mode"] = "rgb_array" if render_mode else None
 
-    env = gym.make_vec(name, num_envs=num_envs, vectorization_mode=vectorization_mode,
-                       wrappers=per_env_wrappers, **make_kwargs)
+    env = gym.make_vec(
+        name,
+        num_envs=num_envs,
+        vectorization_mode=vectorization_mode,
+        wrappers=per_env_wrappers,
+        **make_kwargs,
+    )
 
     if _needs_action_rescaling(env):
         action_space = env.single_action_space
-        logger.info(f"Action rescaling: [{action_space.low.min():.1f}, {action_space.high.max():.1f}] → [-1, 1]")
+        logger.info(
+            f"Action rescaling: [{action_space.low.min():.1f}, {action_space.high.max():.1f}] → [-1, 1]"
+        )
         env = VectorRescaleAction(env, min_action=-1.0, max_action=1.0)
 
     env = VectorRecordEpisodeStatistics(env)
@@ -215,7 +274,9 @@ def preprocess(env):
         if isinstance(clip_reward, (int, float)):
             env = VectorClipReward(env, min_reward=-clip_reward, max_reward=clip_reward)
         else:
-            env = VectorClipReward(env, min_reward=clip_reward[0], max_reward=clip_reward[1])
+            env = VectorClipReward(
+                env, min_reward=clip_reward[0], max_reward=clip_reward[1]
+            )
 
     if render_mode:
         env = VectorRenderAll(env)
@@ -223,9 +284,17 @@ def preprocess(env):
     return env
 
 
-def _make_single_env(name: str, is_atari: bool, render_mode: str | None,
-                     make_kwargs: dict, normalize_obs: bool, normalize_reward: bool,
-                     clip_obs: float | None, clip_reward: float | None, gamma: float) -> gym.Env:
+def _make_single_env(
+    name: str,
+    is_atari: bool,
+    render_mode: str | None,
+    make_kwargs: dict,
+    normalize_obs: bool,
+    normalize_reward: bool,
+    clip_obs: float | None,
+    clip_reward: float | None,
+    gamma: float,
+) -> gym.Env:
     """Create single environment."""
     if is_atari:
         make_kwargs.pop("life_loss_info", None)
@@ -234,15 +303,19 @@ def _make_single_env(name: str, is_atari: bool, render_mode: str | None,
     make_kwargs["render_mode"] = render_mode
     env = gym.make(name, **make_kwargs)
 
-    # Match AtariVectorEnv preprocessing
+    # Match AtariVectorEnv preprocessing (uint8, network handles /255 normalization)
     if is_atari:
         env = FrameStackObservation(
-            AtariPreprocessing(env, frame_skip=1), stack_size=4, padding_type="zero"
+            AtariPreprocessing(env, frame_skip=1, scale_obs=False),
+            stack_size=4,
+            padding_type="zero",
         )
 
     if _needs_action_rescaling(env):
         action_space = env.action_space
-        logger.info(f"Action rescaling: [{action_space.low.min():.1f}, {action_space.high.max():.1f}] → [-1, 1]")
+        logger.info(
+            f"Action rescaling: [{action_space.low.min():.1f}, {action_space.high.max():.1f}] → [-1, 1]"
+        )
         env = RescaleAction(env, min_action=-1.0, max_action=1.0)
 
     env = TrackReward(env)
diff --git a/slm_lab/lib/util.py b/slm_lab/lib/util.py
index 1ba5073da..c8237a17c 100644
--- a/slm_lab/lib/util.py
+++ b/slm_lab/lib/util.py
@@ -4,6 +4,7 @@
 ML-dependent utilities (torch, numpy, cv2) are in ml_util.py and are
 re-exported here for backward compatibility when ML deps are available.
 """
+
 from contextlib import contextmanager
 from datetime import datetime
 from loguru import logger as loguru_logger
@@ -21,8 +22,8 @@
 import ujson
 import yaml
 
-FILE_TS_FORMAT = '%Y_%m_%d_%H%M%S'
-RE_FILE_TS = re.compile(r'(\d{4}_\d{2}_\d{2}_\d{6})')
+FILE_TS_FORMAT = "%Y_%m_%d_%H%M%S"
+RE_FILE_TS = re.compile(r"(\d{4}_\d{2}_\d{2}_\d{6})")
 
 
 def format_metrics(metrics: dict) -> list[str]:
@@ -36,26 +37,26 @@ def format_metrics(metrics: dict) -> list[str]:
     items = []
     for k, v in metrics.items():
         # Convert numpy types to Python types
-        if hasattr(v, 'item'):
+        if hasattr(v, "item"):
             v = v.item()
 
-        if str(v).lower() == 'nan':
-            items.append(f'{k}:nan')
-        elif k == 'frame':
-            items.append(f'{k}:{v:.2e}')
-        elif k in ('total_reward', 'total_reward_ma'):
-            items.append(f'{k}:{v:.2f}')
+        if str(v).lower() == "nan":
+            items.append(f"{k}:nan")
+        elif k == "frame":
+            items.append(f"{k}:{v:.2e}")
+        elif k in ("total_reward", "total_reward_ma"):
+            items.append(f"{k}:{v:.2f}")
         elif isinstance(v, float) and not v.is_integer():
-            items.append(f'{k}:{v:.4g}')
+            items.append(f"{k}:{v:.4g}")
         elif isinstance(v, (int, float)):
-            items.append(f'{k}:{v:g}')
+            items.append(f"{k}:{v:g}")
         else:
-            items.append(f'{k}:{v}')
+            items.append(f"{k}:{v}")
     return items
 
 
 def calc_srs_mean_std(sr_list):
-    '''Given a list of series, calculate their mean and std. Truncates to min length to handle mismatched sizes.'''
+    """Given a list of series, calculate their mean and std. Truncates to min length to handle mismatched sizes."""
     min_len = min(len(sr) for sr in sr_list)
     truncated = [sr.iloc[:min_len].reset_index(drop=True) for sr in sr_list]
     cat_df = pd.DataFrame(dict(enumerate(truncated)))
@@ -65,7 +66,7 @@ def calc_srs_mean_std(sr_list):
 
 
 def calc_ts_diff(ts2, ts1):
-    '''
+    """
     Calculate the time from tss ts1 to ts2
     @param {str} ts2 Later ts in the FILE_TS_FORMAT
     @param {str} ts1 Earlier ts in the FILE_TS_FORMAT
@@ -76,20 +77,22 @@ def calc_ts_diff(ts2, ts1):
     ts2 = '2017_10_17_084740'
     ts_diff = util.calc_ts_diff(ts2, ts1)
     # => '0:00:01'
-    '''
-    delta_t = datetime.strptime(ts2, FILE_TS_FORMAT) - datetime.strptime(ts1, FILE_TS_FORMAT)
+    """
+    delta_t = datetime.strptime(ts2, FILE_TS_FORMAT) - datetime.strptime(
+        ts1, FILE_TS_FORMAT
+    )
     return str(delta_t)
 
 
 def cast_df(val):
-    '''missing pydash method to cast value as DataFrame'''
+    """missing pydash method to cast value as DataFrame"""
     if isinstance(val, pd.DataFrame):
         return val
     return pd.DataFrame(val)
 
 
 def cast_list(val):
-    '''missing pydash method to cast value as list'''
+    """missing pydash method to cast value as list"""
     if ps.is_list(val):
         return val
     else:
@@ -97,23 +100,23 @@ def cast_list(val):
 
 
 def downcast_float32(df):
-    '''Downcast any float64 col to float32 to allow safer pandas comparison'''
+    """Downcast any float64 col to float32 to allow safer pandas comparison"""
     for col in df.columns:
-        if df[col].dtype == 'float':
-            df[col] = df[col].astype('float32')
+        if df[col].dtype == "float":
+            df[col] = df[col].astype("float32")
     return df
 
 
 def frame_mod(frame, frequency, num_envs):
-    '''
+    """
     Generic mod for (frame % frequency == 0) for when num_envs is 1 or more,
-    since frame will increase multiple ticks for vector env, use the remainder'''
+    since frame will increase multiple ticks for vector env, use the remainder"""
     remainder = num_envs or 1
-    return (frame % frequency < remainder)
+    return frame % frequency < remainder
 
 
-def flatten_dict(obj, delim='.'):
-    '''Missing pydash method to flatten dict'''
+def flatten_dict(obj, delim="."):
+    """Missing pydash method to flatten dict"""
     nobj = {}
     for key, val in obj.items():
         if ps.is_dict(val) and not ps.is_empty(val):
@@ -131,7 +134,7 @@ def flatten_dict(obj, delim='.'):
 
 
 def get_class_name(obj, lower=False):
-    '''Get the class name of an object'''
+    """Get the class name of an object"""
     class_name = obj.__class__.__name__
     if lower:
         class_name = class_name.lower()
@@ -139,31 +142,39 @@ def get_class_name(obj, lower=False):
 
 
 def get_file_ext(data_path):
-    '''get the `.ext` of file.ext'''
+    """get the `.ext` of file.ext"""
     return os.path.splitext(data_path)[-1]
 
 
 def get_fn_list(a_cls):
-    '''
+    """
     Get the callable, non-private functions of a class
     @returns {[*str]} A list of strings of fn names
-    '''
-    fn_list = ps.filter_(dir(a_cls), lambda fn: not fn.endswith('__') and callable(getattr(a_cls, fn)))
+    """
+    fn_list = ps.filter_(
+        dir(a_cls), lambda fn: not fn.endswith("__") and callable(getattr(a_cls, fn))
+    )
     return fn_list
 
 
 def get_git_sha():
     try:
-        return subprocess.check_output(['git', 'rev-parse', 'HEAD'], close_fds=True, cwd=ROOT_DIR).decode().strip()
+        return (
+            subprocess.check_output(
+                ["git", "rev-parse", "HEAD"], close_fds=True, cwd=ROOT_DIR
+            )
+            .decode()
+            .strip()
+        )
     except (subprocess.CalledProcessError, FileNotFoundError):
-        return 'unknown'  # git not available (e.g., in minimal/remote env)
+        return "unknown"  # git not available (e.g., in minimal/remote env)
 
 
 def get_port():
-    '''Get a unique port number for a run time as 4xxx, where xxx is the last 3 digits from the PID, front-padded with 0'''
+    """Get a unique port number for a run time as 4xxx, where xxx is the last 3 digits from the PID, front-padded with 0"""
     # get 3 digits from pid
     xxx = ps.pad_start(str(os.getpid())[-3:], 3, 0)
-    port = int(f'4{xxx}')
+    port = int(f"4{xxx}")
     return port
 
 
@@ -174,37 +185,38 @@ def get_predir(spec):
     predir = f"data/{spec_name}_{meta_spec['experiment_ts']}"
     return predir
 
+
 def get_experiment_ts(path):
     """Extract experiment timestamp from a path like 'data/exp_name_2025_09_26_123456'."""
     matches = RE_FILE_TS.findall(path)
     return matches[0] if matches else None
 
 
-def get_prepath(spec, unit='experiment'):
-    spec_name = spec['name']
-    meta_spec = spec['meta']
-    predir = f'data/{spec_name}_{meta_spec["experiment_ts"]}'
-    prename = f'{spec_name}'
-    trial_index = meta_spec['trial']
-    session_index = meta_spec['session']
-    t_str = '' if trial_index is None else f'_t{trial_index}'
-    s_str = '' if session_index is None else f'_s{session_index}'
-    if unit == 'trial':
+def get_prepath(spec, unit="experiment"):
+    spec_name = spec["name"]
+    meta_spec = spec["meta"]
+    predir = f"data/{spec_name}_{meta_spec['experiment_ts']}"
+    prename = f"{spec_name}"
+    trial_index = meta_spec["trial"]
+    session_index = meta_spec["session"]
+    t_str = "" if trial_index is None else f"_t{trial_index}"
+    s_str = "" if session_index is None else f"_s{session_index}"
+    if unit == "trial":
         prename += t_str
-    elif unit == 'session':
-        prename += f'{t_str}{s_str}'
-    prepath = f'{predir}/{prename}'
+    elif unit == "session":
+        prename += f"{t_str}{s_str}"
+    prepath = f"{predir}/{prename}"
     return prepath
 
 
 def get_session_df_path(session_spec, df_mode):
-    '''Method to return standard filepath for session_df (agent.mt.train_df/eval_df) for saving and loading'''
-    info_prepath = session_spec['meta']['info_prepath']
-    return f'{info_prepath}_session_df_{df_mode}.csv'
+    """Method to return standard filepath for session_df (agent.mt.train_df/eval_df) for saving and loading"""
+    info_prepath = session_spec["meta"]["info_prepath"]
+    return f"{info_prepath}_session_df_{df_mode}.csv"
 
 
 def get_ts(pattern=FILE_TS_FORMAT):
-    '''
+    """
     Get current ts, defaults to format used for filename
     @param {str} pattern To format the ts
     @returns {str} ts
@@ -212,7 +224,7 @@ def get_ts(pattern=FILE_TS_FORMAT):
 
     util.get_ts()
     # => '2017_10_17_084739'
-    '''
+    """
     ts_obj = datetime.now()
     ts = ts_obj.strftime(pattern)
     assert RE_FILE_TS.search(ts)
@@ -220,25 +232,25 @@ def get_ts(pattern=FILE_TS_FORMAT):
 
 
 def insert_folder(prepath, folder):
-    '''Insert a folder into prepath'''
-    split_path = prepath.split('/')
+    """Insert a folder into prepath"""
+    split_path = prepath.split("/")
     prename = split_path.pop()
     split_path += [folder, prename]
-    return '/'.join(split_path)
+    return "/".join(split_path)
 
 
 def in_eval_lab_mode():
-    '''Check if lab_mode is one of EVAL_MODES'''
+    """Check if lab_mode is one of EVAL_MODES"""
     return lab_mode() in EVAL_MODES
 
 
 def in_train_lab_mode():
-    '''Check if lab_mode is one of TRAIN_MODES'''
+    """Check if lab_mode is one of TRAIN_MODES"""
     return lab_mode() in TRAIN_MODES
 
 
 def is_jupyter():
-    '''Check if process is in Jupyter kernel'''
+    """Check if process is in Jupyter kernel"""
     try:
         get_ipython().config
         return True
@@ -249,7 +261,7 @@ def is_jupyter():
 
 @contextmanager
 def ctx_lab_mode(lab_mode):
-    '''
+    """
     Creates context to run method with a specific lab_mode
     @example
     with util.ctx_lab_mode('eval'):
@@ -258,34 +270,34 @@ def ctx_lab_mode(lab_mode):
     @util.ctx_lab_mode('eval')
     def foo():
         ...
-    '''
-    prev_lab_mode = os.environ.get('lab_mode')
-    os.environ['lab_mode'] = lab_mode
+    """
+    prev_lab_mode = os.environ.get("lab_mode")
+    os.environ["lab_mode"] = lab_mode
     yield
     if prev_lab_mode is None:
-        del os.environ['lab_mode']
+        del os.environ["lab_mode"]
     else:
-        os.environ['lab_mode'] = prev_lab_mode
+        os.environ["lab_mode"] = prev_lab_mode
 
 
 def monkey_patch(base_cls, extend_cls):
-    '''Monkey patch a base class with methods from extend_cls'''
+    """Monkey patch a base class with methods from extend_cls"""
     ext_fn_list = get_fn_list(extend_cls)
     for fn in ext_fn_list:
         setattr(base_cls, fn, getattr(extend_cls, fn))
 
 
 def prepath_to_idxs(prepath):
-    '''Extract trial index and session index from prepath if available'''
-    tidxs = re.findall(r'_t(\d+)', prepath)
+    """Extract trial index and session index from prepath if available"""
+    tidxs = re.findall(r"_t(\d+)", prepath)
     trial_index = int(tidxs[0]) if tidxs else None
-    sidxs = re.findall(r'_s(\d+)', prepath)
+    sidxs = re.findall(r"_s(\d+)", prepath)
     session_index = int(sidxs[0]) if sidxs else None
     return trial_index, session_index
 
 
 def read(data_path, **kwargs):
-    '''
+    """
     Universal data reading method with smart data parsing
     - {.csv} to DataFrame
     - {.json} to dict, list
@@ -307,14 +319,14 @@ def read(data_path, **kwargs):
 
     data_str = util.read('test/fixture/lib/util/test_str.txt')
     # => <str>
-    '''
+    """
     data_path = smart_path(data_path)
     try:
         assert os.path.isfile(data_path)
     except AssertionError:
         raise FileNotFoundError(data_path)
     ext = get_file_ext(data_path)
-    if ext == '.csv':
+    if ext == ".csv":
         data = read_as_df(data_path, **kwargs)
     else:
         data = read_as_plain(data_path, **kwargs)
@@ -322,20 +334,18 @@ def read(data_path, **kwargs):
 
 
 def read_as_df(data_path, **kwargs):
-    '''Submethod to read data as DataFrame'''
+    """Submethod to read data as DataFrame"""
     data = pd.read_csv(data_path, **kwargs)
     return data
 
 
-
-
 def read_as_plain(data_path, **kwargs):
-    '''Submethod to read data as plain type'''
-    open_file = open(data_path, 'r')
+    """Submethod to read data as plain type"""
+    open_file = open(data_path, "r")
     ext = get_file_ext(data_path)
-    if ext == '.json':
+    if ext == ".json":
         data = ujson.load(open_file, **kwargs)
-    elif ext == '.yml':
+    elif ext in (".yml", ".yaml"):
         data = yaml.load(open_file, Loader=yaml.FullLoader, **kwargs)
     else:
         data = open_file.read()
@@ -344,32 +354,35 @@ def read_as_plain(data_path, **kwargs):
 
 
 def log_dict(data: dict, title: str = None):
-    '''Log dict as clean YAML format.'''
-    lines = [f'{title}:'] if title else []
+    """Log dict as clean YAML format."""
+    lines = [f"{title}:"] if title else []
     for k, v in data.items():
         if isinstance(v, dict):
-            yaml_str = yaml.dump({k: v}, default_flow_style=False, indent=2, sort_keys=False).rstrip()
+            yaml_str = yaml.dump(
+                {k: v}, default_flow_style=False, indent=2, sort_keys=False
+            ).rstrip()
             lines.append(yaml_str)
         elif v is not None and not ps.reg_exp_js_match(str(v), "/<.+>/"):
-            lines.append(f'{k}: {v}')
-    logger.info('\n'.join(lines))
+            lines.append(f"{k}: {v}")
+    logger.info("\n".join(lines))
 
 
 def log_self_desc(cls, omit=None):
-    '''Log self description in YAML-style format.'''
+    """Log self description in YAML-style format."""
     try:
         from slm_lab.lib.ml_util import get_class_attr
+
         obj_dict = get_class_attr(cls)
     except ImportError:
         # Fallback for minimal install (no torch)
-        obj_dict = {k: str(v) for k, v in cls.__dict__.items() if not k.startswith('_')}
+        obj_dict = {k: str(v) for k, v in cls.__dict__.items() if not k.startswith("_")}
     if omit:
         obj_dict = ps.omit(obj_dict, omit)
     log_dict(obj_dict, get_class_name(cls))
 
 
 def set_attr(obj, attr_dict, keys=None):
-    '''Set attribute of an object from a dict'''
+    """Set attribute of an object from a dict"""
     if keys is not None:
         attr_dict = ps.pick(attr_dict, keys)
     for attr, val in attr_dict.items():
@@ -378,9 +391,9 @@ def set_attr(obj, attr_dict, keys=None):
 
 
 def set_logger(spec, logger, unit=None):
-    '''Set the logger for a lab unit give its spec'''
-    os.environ['LOG_PREPATH'] = insert_folder(get_prepath(spec, unit=unit), 'log')
-    log_filepath = os.path.join(ROOT_DIR, os.environ['LOG_PREPATH'] + '.log')
+    """Set the logger for a lab unit give its spec"""
+    os.environ["LOG_PREPATH"] = insert_folder(get_prepath(spec, unit=unit), "log")
+    log_filepath = os.path.join(ROOT_DIR, os.environ["LOG_PREPATH"] + ".log")
     os.makedirs(os.path.dirname(log_filepath), exist_ok=True)
 
     # Remove existing file handlers (stdout remains)
@@ -390,14 +403,14 @@ def set_logger(spec, logger, unit=None):
     loguru_logger.add(
         log_filepath,
         format=logger.LOG_FORMAT,
-        level='INFO',
+        level="INFO",
         backtrace=True,
-        diagnose=True
+        diagnose=True,
     )
 
 
 def _sizeof(obj, seen=None):
-    '''Recursively finds size of objects'''
+    """Recursively finds size of objects"""
     size = sys.getsizeof(obj)
     if seen is None:
         seen = set()
@@ -410,20 +423,20 @@ def _sizeof(obj, seen=None):
     if isinstance(obj, dict):
         size += sum([_sizeof(v, seen) for v in obj.values()])
         size += sum([_sizeof(k, seen) for k in obj.keys()])
-    elif hasattr(obj, '__dict__'):
+    elif hasattr(obj, "__dict__"):
         size += _sizeof(obj.__dict__, seen)
-    elif hasattr(obj, '__iter__') and not isinstance(obj, (str, bytes, bytearray)):
+    elif hasattr(obj, "__iter__") and not isinstance(obj, (str, bytes, bytearray)):
         size += sum([_sizeof(i, seen) for i in obj])
     return size
 
 
 def sizeof(obj, divisor=1e6):
-    '''Return the size of object, in MB by default'''
+    """Return the size of object, in MB by default"""
     return _sizeof(obj) / divisor
 
 
 def smart_path(data_path, as_dir=False):
-    '''
+    """
     Resolve data_path into abspath with fallback to join from ROOT_DIR
     @param {str} data_path The input data path to resolve
     @param {bool} as_dir Whether to return as dirname
@@ -435,7 +448,7 @@ def smart_path(data_path, as_dir=False):
 
     util.smart_path('/tmp')
     # => '/tmp'
-    '''
+    """
     if not os.path.isabs(data_path):
         data_path = os.path.join(ROOT_DIR, data_path)
     if as_dir:
@@ -444,7 +457,7 @@ def smart_path(data_path, as_dir=False):
 
 
 def write(data, data_path):
-    '''
+    """
     Universal data writing method with smart data parsing
     - {.csv} from DataFrame
     - {.json} from dict, list
@@ -463,12 +476,12 @@ def write(data, data_path):
     data_path = util.write(data_list, 'test/fixture/lib/util/test_list.json')
 
     data_path = util.write(data_str, 'test/fixture/lib/util/test_str.txt')
-    '''
+    """
     data_path = smart_path(data_path)
     data_dir = os.path.dirname(data_path)
     os.makedirs(data_dir, exist_ok=True)
     ext = get_file_ext(data_path)
-    if ext == '.csv':
+    if ext == ".csv":
         write_as_df(data, data_path)
     else:
         write_as_plain(data, data_path)
@@ -476,26 +489,25 @@ def write(data, data_path):
 
 
 def write_as_df(data, data_path):
-    '''Submethod to write data as DataFrame'''
+    """Submethod to write data as DataFrame"""
     df = cast_df(data)
     df.to_csv(data_path, index=False)
     return data_path
 
 
-
-
 def write_as_plain(data, data_path):
-    '''Submethod to write data as plain type'''
-    open_file = open(data_path, 'w')
+    """Submethod to write data as plain type"""
+    open_file = open(data_path, "w")
     ext = get_file_ext(data_path)
-    if ext == '.json':
+    if ext == ".json":
         try:
             from slm_lab.lib.ml_util import LabJsonEncoder
+
             json.dump(data, open_file, indent=2, cls=LabJsonEncoder)
         except ImportError:
             # Fallback for minimal install (no numpy)
             json.dump(data, open_file, indent=2)
-    elif ext == '.yml':
+    elif ext in (".yml", ".yaml"):
         yaml.dump(data, open_file)
     else:
         open_file.write(str(data))
diff --git a/slm_lab/spec/benchmark/ppo/ppo_cartpole.yaml b/slm_lab/spec/benchmark/ppo/ppo_cartpole.yaml
new file mode 100644
index 000000000..b763db292
--- /dev/null
+++ b/slm_lab/spec/benchmark/ppo/ppo_cartpole.yaml
@@ -0,0 +1,58 @@
+ppo_cartpole:
+  agent:
+    name: PPO
+    algorithm:
+      name: PPO
+      action_pdtype: default
+      action_policy: default
+      explore_var_spec: null
+      gamma: 0.9769
+      lam: 0.9112
+      clip_eps_spec:
+        name: no_decay
+        start_val: 0.1312
+        end_val: 0.1312
+        start_step: 0
+        end_step: 200000
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.0074
+        end_val: 0.0074
+        start_step: 0
+        end_step: 200000
+      val_loss_coef: 0.6049
+      clip_val_loss: false
+      time_horizon: 256
+      minibatch_size: 128
+      training_epoch: 15
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: MLPNet
+      shared: false
+      hid_layers:
+        - 64
+        - 64
+      hid_layers_activation: tanh
+      clip_grad_val: 0.5
+      use_same_optim: false
+      loss_spec:
+        name: MSELoss
+      actor_optim_spec:
+        name: Adam
+        lr: 0.0005909
+      critic_optim_spec:
+        name: Adam
+        lr: 0.0006352
+      gpu: auto
+  env:
+    name: CartPole-v1
+    max_t: null
+    max_frame: 200000
+    num_envs: 4
+  meta:
+    distributed: false
+    log_frequency: 500
+    eval_frequency: 256
+    max_session: 4
+    max_trial: 1
diff --git a/slm_lab/spec/benchmark/sac/sac_cartpole.yaml b/slm_lab/spec/benchmark/sac/sac_cartpole.yaml
new file mode 100644
index 000000000..420dc567c
--- /dev/null
+++ b/slm_lab/spec/benchmark/sac/sac_cartpole.yaml
@@ -0,0 +1,40 @@
+sac_cartpole:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: Categorical
+      action_policy: default
+      gamma: 0.99
+      training_frequency: 1
+      training_start_step: 1000
+    memory:
+      name: Replay
+      batch_size: 256
+      max_size: 100000
+      use_cer: false
+    net:
+      type: MLPNet
+      hid_layers: [256, 256]
+      hid_layers_activation: relu
+      init_fn: xavier_uniform_
+      clip_grad_val: null
+      loss_spec:
+        name: MSELoss
+      optim_spec:
+        name: Adam
+        lr: 0.0003
+      update_type: polyak
+      polyak_coef: 0.005
+      gpu: auto
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta:
+    distributed: false
+    log_frequency: 500
+    eval_frequency: 500
+    max_session: 4
+    max_trial: 1
diff --git a/slm_lab/spec/benchmark_arc/a2c/a2c_atari_arc.yaml b/slm_lab/spec/benchmark_arc/a2c/a2c_atari_arc.yaml
new file mode 100644
index 000000000..487aec0fe
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/a2c/a2c_atari_arc.yaml
@@ -0,0 +1,77 @@
+# A2C Atari - TorchArcNet specs
+# ConvNet body: LazyConv2d(32,8,4) + ReLU + LazyConv2d(64,4,2) + ReLU + LazyConv2d(64,3,1) + ReLU + Flatten + LazyLinear(512) + ReLU
+
+a2c_gae_atari_arc:
+  agent:
+    name: A2C
+    algorithm:
+      name: ActorCritic
+      action_pdtype: default
+      action_policy: default
+      explore_var_spec: null
+      gamma: 0.99
+      lam: 0.95
+      num_step_returns: null
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.01
+        end_val: 0.01
+        start_step: 0
+        end_step: 0
+      val_loss_coef: 0.5
+      training_frequency: 32
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      shared: true
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyConv2d: {out_channels: 32, kernel_size: 8, stride: 4}
+              - ReLU:
+              - LazyConv2d: {out_channels: 64, kernel_size: 4, stride: 2}
+              - ReLU:
+              - LazyConv2d: {out_channels: 64, kernel_size: 3, stride: 1}
+              - ReLU:
+              - Flatten:
+              - LazyLinear: {out_features: 512}
+              - ReLU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      hid_layers_activation: relu
+      init_fn: orthogonal_
+      actor_init_std: 0.01
+      critic_init_std: 1.0
+      batch_norm: false
+      clip_grad_val: 0.5
+      use_same_optim: false
+      loss_spec:
+        name: MSELoss
+      actor_optim_spec:
+        name: RMSprop
+        lr: 7.0e-4
+        alpha: 0.99
+        eps: 1.0e-5
+      critic_optim_spec:
+        name: RMSprop
+        lr: 7.0e-4
+        alpha: 0.99
+        eps: 1.0e-5
+      normalize: true
+      gpu: auto
+  env:
+    name: "${env}"
+    num_envs: 16
+    max_frame: 1.0e+7
+    life_loss_info: true
+  meta:
+    distributed: false
+    eval_frequency: 10000
+    log_frequency: 10000
+    max_session: 4
+    max_trial: 1
diff --git a/slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml b/slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml
new file mode 100644
index 000000000..0fc83ef07
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/a2c/a2c_classic_arc.yaml
@@ -0,0 +1,311 @@
+# A2C Classic Control - TorchArcNet specs
+# Covers: CartPole-v1, Acrobot-v1, Pendulum-v1, LunarLander-v3 (discrete + continuous)
+
+# -- Shared anchors --
+
+_a2c_classic_meta: &a2c_classic_meta
+  distributed: false
+  log_frequency: 500
+  eval_frequency: 1000
+  max_session: 4
+  max_trial: 1
+
+_a2c_classic_net: &a2c_classic_net
+  type: TorchArcNet
+  shared: false
+  arc:
+    modules:
+      body:
+        Sequential:
+          - LazyLinear: {out_features: 64}
+          - Tanh:
+          - LazyLinear: {out_features: 64}
+          - Tanh:
+    graph:
+      input: x
+      modules:
+        body: [x]
+      output: body
+  hid_layers_activation: tanh
+  init_fn: orthogonal_
+  actor_init_std: 0.01
+  critic_init_std: 1.0
+  batch_norm: false
+  clip_grad_val: 0.5
+  use_same_optim: false
+  loss_spec:
+    name: MSELoss
+  gpu: auto
+
+_a2c_classic_algorithm: &a2c_classic_algorithm
+  name: ActorCritic
+  action_pdtype: default
+  action_policy: default
+  explore_var_spec: null
+  gamma: 0.99
+  lam: 0.95
+  num_step_returns: null
+  entropy_coef_spec: &entropy_linear_001
+    name: linear_decay
+    start_val: 0.01
+    end_val: 0.0
+    start_step: 0
+    end_step: 200000
+  val_loss_coef: 0.5
+  training_frequency: 32
+
+# -- Specs --
+
+a2c_gae_cartpole_arc:
+  agent:
+    name: A2C
+    algorithm:
+      <<: *a2c_classic_algorithm
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *a2c_classic_net
+      actor_optim_spec:
+        name: Adam
+        lr: 0.001
+      critic_optim_spec:
+        name: Adam
+        lr: 0.003
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta:
+    <<: *a2c_classic_meta
+
+a2c_gae_acrobot_arc:
+  agent:
+    name: A2C
+    algorithm:
+      <<: *a2c_classic_algorithm
+      entropy_coef_spec:
+        name: linear_decay
+        start_val: 0.01
+        end_val: 0.0
+        start_step: 0
+        end_step: 300000
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *a2c_classic_net
+      actor_optim_spec:
+        name: Adam
+        lr: 0.001
+      critic_optim_spec:
+        name: Adam
+        lr: 0.003
+  env:
+    name: Acrobot-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 300000
+  meta:
+    <<: *a2c_classic_meta
+    eval_frequency: 5000
+
+a2c_gae_pendulum_arc:
+  agent:
+    name: A2C
+    algorithm:
+      <<: *a2c_classic_algorithm
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.0
+        end_val: 0.0
+        start_step: 0
+        end_step: 0
+      training_frequency: 2048
+      normalize_v_targets: true
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      shared: false
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 256}
+              - ReLU:
+              - LazyLinear: {out_features: 256}
+              - ReLU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      hid_layers_activation: relu
+      init_fn: orthogonal_
+      actor_init_std: 0.01
+      critic_init_std: 1.0
+      batch_norm: false
+      clip_grad_val: 0.5
+      use_same_optim: true
+      loss_spec:
+        name: MSELoss
+      optim_spec:
+        name: Adam
+        lr: 2.0e-3
+      gpu: auto
+  env:
+    name: Pendulum-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 300000
+    normalize_obs: true
+  meta:
+    <<: *a2c_classic_meta
+
+a2c_gae_lunar_arc:
+  agent:
+    name: A2C
+    algorithm:
+      <<: *a2c_classic_algorithm
+      gamma: 0.995
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.00001
+        end_val: 0.00001
+        start_step: 0
+        end_step: 0
+      training_frequency: 5
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      shared: false
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 128}
+              - Tanh:
+              - LazyLinear: {out_features: 128}
+              - Tanh:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      hid_layers_activation: tanh
+      init_fn: orthogonal_
+      actor_init_std: 0.01
+      critic_init_std: 1.0
+      batch_norm: false
+      clip_grad_val: 0.5
+      use_same_optim: false
+      loss_spec:
+        name: MSELoss
+      actor_optim_spec:
+        name: Adam
+        lr: 0.00083
+      critic_optim_spec:
+        name: Adam
+        lr: 0.00083
+      lr_scheduler_spec:
+        name: LinearToZero
+        frame: 300000
+      gpu: auto
+  env:
+    name: LunarLander-v3
+    num_envs: 8
+    max_t: null
+    max_frame: 300000
+  meta:
+    <<: *a2c_classic_meta
+    log_frequency: 1000
+    eval_frequency: 5000
+    max_trial: 8
+    search_resources:
+      cpu: 1
+      gpu: 0.125
+    search_scheduler:
+      grace_period: 50000
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.99, 0.999]
+    agent.algorithm.lam__uniform: [0.92, 0.98]
+    agent.algorithm.training_frequency__qrandint: [5, 16, 1]
+    agent.algorithm.entropy_coef_spec.start_val__loguniform: [0.000001, 0.0001]
+    agent.net.actor_optim_spec.lr__loguniform: [0.0003, 0.002]
+    agent.net.critic_optim_spec.lr__loguniform: [0.0003, 0.002]
+
+a2c_gae_lunar_continuous_arc:
+  agent:
+    name: A2C
+    algorithm:
+      <<: *a2c_classic_algorithm
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.01
+        end_val: 0.01
+        start_step: 0
+        end_step: 0
+      training_frequency: 64
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      shared: false
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 128}
+              - Tanh:
+              - LazyLinear: {out_features: 128}
+              - Tanh:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      hid_layers_activation: tanh
+      init_fn: orthogonal_
+      actor_init_std: 0.01
+      critic_init_std: 1.0
+      batch_norm: false
+      clip_grad_val: 0.5
+      use_same_optim: false
+      loss_spec:
+        name: MSELoss
+      actor_optim_spec:
+        name: Adam
+        lr: 0.0007
+      critic_optim_spec:
+        name: Adam
+        lr: 0.001
+      lr_scheduler_spec:
+        name: LinearToZero
+        frame: 300000
+      gpu: auto
+  env:
+    name: LunarLander-v3
+    continuous: true
+    num_envs: 8
+    max_t: null
+    max_frame: 300000
+  meta:
+    <<: *a2c_classic_meta
+    log_frequency: 1000
+    eval_frequency: 5000
+    max_trial: 8
+    search_resources:
+      cpu: 1
+      gpu: 0.125
+    search_scheduler:
+      grace_period: 50000
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.98, 0.999]
+    agent.algorithm.lam__uniform: [0.92, 0.98]
+    agent.algorithm.training_frequency__qrandint: [32, 128, 16]
+    agent.algorithm.entropy_coef_spec.start_val__loguniform: [0.005, 0.02]
+    agent.net.actor_optim_spec.lr__loguniform: [0.0003, 0.003]
+    agent.net.critic_optim_spec.lr__loguniform: [0.0005, 0.003]
diff --git a/slm_lab/spec/benchmark_arc/dqn/dqn_box2d_arc.yaml b/slm_lab/spec/benchmark_arc/dqn/dqn_box2d_arc.yaml
new file mode 100644
index 000000000..80bcf3f56
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/dqn/dqn_box2d_arc.yaml
@@ -0,0 +1,180 @@
+# DQN Box2D - TorchArcNet specs
+# Covers: LunarLander-v3 (DQN, DDQN+PER), MountainCar-v0 (DDQN+PER)
+
+# -- Shared anchors --
+
+_dqn_box2d_meta: &dqn_box2d_meta
+  distributed: false
+  log_frequency: 1000
+  eval_frequency: 1000
+  max_session: 4
+  max_trial: 1
+
+_dqn_box2d_net: &dqn_box2d_net
+  type: TorchArcNet
+  arc:
+    modules:
+      body:
+        Sequential:
+          - LazyLinear: {out_features: 256}
+          - ReLU:
+          - LazyLinear: {out_features: 128}
+          - ReLU:
+    graph:
+      input: x
+      modules:
+        body: [x]
+      output: body
+  hid_layers_activation: relu
+  clip_grad_val: 10.0
+  loss_spec:
+    name: SmoothL1Loss
+  optim_spec:
+    name: AdamW
+    lr: 0.000724
+  update_type: replace
+  update_frequency: 100
+  gpu: auto
+
+_dqn_per_replay: &dqn_per_replay
+  name: PrioritizedReplay
+  alpha: 0.6
+  epsilon: 0.0001
+  batch_size: 32
+  max_size: 50000
+  use_cer: false
+
+# -- LunarLander specs --
+
+dqn_concat_lunar_arc:
+  agent:
+    name: DQN
+    algorithm:
+      name: DQN
+      action_pdtype: Argmax
+      action_policy: epsilon_greedy
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.039
+        start_step: 0
+        end_step: 50000
+      gamma: 0.982
+      training_batch_iter: 1
+      training_iter: 4
+      training_frequency: 1
+      training_start_step: 32
+    memory:
+      name: Replay
+      batch_size: 32
+      max_size: 50000
+      use_cer: false
+    net:
+      <<: *dqn_box2d_net
+  env:
+    name: LunarLander-v3
+    max_t: null
+    max_frame: 300000
+    num_envs: 8
+  meta:
+    <<: *dqn_box2d_meta
+    max_trial: 12
+    search_scheduler:
+      grace_period: 50000
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.98, 0.999]
+    agent.algorithm.explore_var_spec.end_val__uniform: [0.01, 0.05]
+    agent.net.optim_spec.lr__loguniform: [0.0003, 0.003]
+    agent.net.update_frequency__qrandint: [50, 150, 25]
+
+ddqn_per_concat_lunar_arc:
+  agent:
+    name: DoubleDQN
+    algorithm:
+      name: DoubleDQN
+      action_pdtype: Argmax
+      action_policy: epsilon_greedy
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.01
+        start_step: 0
+        end_step: 50000
+      gamma: 0.99
+      training_batch_iter: 1
+      training_iter: 4
+      training_frequency: 1
+      training_start_step: 32
+    memory:
+      <<: *dqn_per_replay
+    net:
+      <<: *dqn_box2d_net
+      optim_spec:
+        name: AdamW
+        lr: 0.00025
+  env:
+    name: LunarLander-v3
+    num_envs: 8
+    max_t: null
+    max_frame: 300000
+  meta:
+    <<: *dqn_box2d_meta
+
+# -- MountainCar DDQN+PER --
+
+ddqn_per_mountaincar_arc:
+  agent:
+    name: DoubleDQN
+    algorithm:
+      name: DoubleDQN
+      action_pdtype: Argmax
+      action_policy: epsilon_greedy
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.01
+        start_step: 0
+        end_step: 50000
+      gamma: 0.99
+      training_batch_iter: 2
+      training_iter: 2
+      training_frequency: 4
+      training_start_step: 32
+    memory:
+      <<: *dqn_per_replay
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 128}
+              - ReLU:
+              - LazyLinear: {out_features: 128}
+              - ReLU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      hid_layers_activation: relu
+      clip_grad_val: 1.0
+      loss_spec:
+        name: SmoothL1Loss
+      optim_spec:
+        name: Adam
+        lr: 0.0005
+      update_type: replace
+      update_frequency: 100
+      gpu: auto
+  env:
+    name: MountainCar-v0
+    max_t: null
+    max_frame: 500000
+  meta:
+    distributed: false
+    eval_frequency: 10000
+    max_session: 4
+    max_trial: 1
+    log_frequency: 500
diff --git a/slm_lab/spec/benchmark_arc/dqn/dqn_classic_arc.yaml b/slm_lab/spec/benchmark_arc/dqn/dqn_classic_arc.yaml
new file mode 100644
index 000000000..509fc8870
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/dqn/dqn_classic_arc.yaml
@@ -0,0 +1,428 @@
+# DQN Classic Control - TorchArcNet specs
+# Covers: CartPole-v1 (VanillaDQN, DQN, DDQN+PER), Acrobot-v1, MountainCar-v0
+
+# -- Shared anchors --
+
+_dqn_classic_meta: &dqn_classic_meta
+  distributed: false
+  eval_frequency: 500
+  max_session: 4
+  max_trial: 1
+  log_frequency: 500
+
+_dqn_net_128x2_relu: &dqn_net_128x2_relu
+  type: TorchArcNet
+  arc:
+    modules:
+      body:
+        Sequential:
+          - LazyLinear: {out_features: 128}
+          - ReLU:
+          - LazyLinear: {out_features: 128}
+          - ReLU:
+    graph:
+      input: x
+      modules:
+        body: [x]
+      output: body
+  hid_layers_activation: relu
+  clip_grad_val: 1.0
+  loss_spec:
+    name: SmoothL1Loss
+  optim_spec:
+    name: Adam
+    lr: 0.0005
+  update_type: replace
+  update_frequency: 100
+  gpu: auto
+
+_dqn_replay: &dqn_replay
+  name: Replay
+  batch_size: 64
+  max_size: 50000
+  use_cer: true
+
+_dqn_per_replay: &dqn_per_replay
+  name: PrioritizedReplay
+  alpha: 0.6
+  epsilon: 0.0001
+  batch_size: 32
+  max_size: 50000
+  use_cer: false
+
+# -- CartPole specs --
+
+vanilla_dqn_boltzmann_cartpole_arc:
+  agent:
+    name: VanillaDQN
+    algorithm:
+      name: VanillaDQN
+      action_pdtype: Categorical
+      action_policy: boltzmann
+      explore_var_spec:
+        name: linear_decay
+        start_val: 5.0
+        end_val: 0.5
+        start_step: 0
+        end_step: 4000
+      gamma: 0.99
+      training_batch_iter: 2
+      training_iter: 2
+      training_frequency: 1
+      training_start_step: 32
+    memory:
+      name: Replay
+      batch_size: 32
+      max_size: 10000
+      use_cer: false
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 64}
+              - SELU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      clip_grad_val: 0.5
+      loss_spec:
+        name: MSELoss
+      optim_spec:
+        name: AdamW
+        lr: 0.01
+      gpu: auto
+  env:
+    name: CartPole-v1
+    max_t: null
+    max_frame: 30000
+    num_envs: 4
+  meta:
+    <<: *dqn_classic_meta
+
+dqn_boltzmann_cartpole_arc:
+  agent:
+    name: DQN
+    algorithm:
+      name: DQN
+      action_pdtype: Categorical
+      action_policy: boltzmann
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.08
+        start_step: 0
+        end_step: 12000
+      gamma: 0.995
+      training_batch_iter: 2
+      training_iter: 2
+      training_frequency: 1
+      training_start_step: 32
+    memory:
+      <<: *dqn_replay
+      batch_size: 64
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 256}
+              - SELU:
+              - LazyLinear: {out_features: 128}
+              - SELU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      clip_grad_val: 0.55
+      loss_spec:
+        name: SmoothL1Loss
+      optim_spec:
+        name: AdamW
+        lr: 0.0003
+      update_type: replace
+      update_frequency: 75
+      gpu: auto
+  env:
+    name: CartPole-v1
+    max_t: null
+    max_frame: 200000
+    num_envs: 4
+  meta:
+    <<: *dqn_classic_meta
+    log_frequency: 500
+    eval_frequency: 1000
+    max_trial: 16
+    search_scheduler:
+      grace_period: 20000
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.95, 0.999]
+    agent.algorithm.explore_var_spec.end_val__uniform: [0.02, 0.15]
+    agent.net.optim_spec.lr__loguniform: [0.0001, 0.003]
+    agent.net.update_frequency__qrandint: [50, 150, 25]
+
+ddqn_per_boltzmann_cartpole_arc:
+  agent:
+    name: DoubleDQN
+    algorithm:
+      name: DoubleDQN
+      action_pdtype: Categorical
+      action_policy: boltzmann
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.08
+        start_step: 0
+        end_step: 12000
+      gamma: 0.995
+      training_batch_iter: 2
+      training_iter: 2
+      training_frequency: 1
+      training_start_step: 32
+    memory:
+      <<: *dqn_per_replay
+      batch_size: 64
+      use_cer: true
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 256}
+              - SELU:
+              - LazyLinear: {out_features: 128}
+              - SELU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      clip_grad_val: 0.55
+      loss_spec:
+        name: SmoothL1Loss
+      optim_spec:
+        name: AdamW
+        lr: 0.0003
+      update_type: replace
+      update_frequency: 75
+      gpu: auto
+  env:
+    name: CartPole-v1
+    max_t: null
+    max_frame: 200000
+    num_envs: 4
+  meta:
+    <<: *dqn_classic_meta
+    log_frequency: 500
+    eval_frequency: 1000
+
+dqn_epsilon_greedy_cartpole_arc:
+  agent:
+    name: DQN
+    algorithm:
+      name: DQN
+      action_pdtype: Argmax
+      action_policy: epsilon_greedy
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.1
+        start_step: 0
+        end_step: 3000
+      gamma: 0.99
+      training_batch_iter: 2
+      training_iter: 2
+      training_frequency: 1
+      training_start_step: 32
+    memory:
+      name: Replay
+      batch_size: 32
+      max_size: 50000
+      use_cer: true
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 256}
+              - SELU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      clip_grad_val: 0.5
+      loss_spec:
+        name: SmoothL1Loss
+      optim_spec:
+        name: AdamW
+        lr: 0.0003
+      update_type: replace
+      update_frequency: 100
+      gpu: auto
+  env:
+    name: CartPole-v1
+    max_t: null
+    max_frame: 200000
+    num_envs: 4
+  meta:
+    <<: *dqn_classic_meta
+    eval_frequency: 1000
+
+# -- Acrobot specs --
+
+dqn_epsilon_greedy_acrobot_arc:
+  agent:
+    name: DQN
+    algorithm:
+      name: DQN
+      action_pdtype: Argmax
+      action_policy: epsilon_greedy
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.027
+        start_step: 0
+        end_step: 20000
+      gamma: 0.987
+      training_batch_iter: 2
+      training_iter: 2
+      training_frequency: 1
+      training_start_step: 32
+    memory:
+      <<: *dqn_replay
+    net:
+      <<: *dqn_net_128x2_relu
+      optim_spec:
+        name: Adam
+        lr: 0.00108
+  env:
+    name: Acrobot-v1
+    max_t: null
+    max_frame: 300000
+    num_envs: 4
+  meta:
+    <<: *dqn_classic_meta
+    max_trial: 12
+    search_scheduler:
+      grace_period: 50000
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.99, 0.999]
+    agent.algorithm.explore_var_spec.end_val__uniform: [0.05, 0.15]
+    agent.algorithm.explore_var_spec.end_step__qrandint: [10000, 20000, 5000]
+    agent.net.optim_spec.lr__loguniform: [0.0003, 0.001]
+
+dqn_boltzmann_acrobot_arc:
+  agent:
+    name: DQN
+    algorithm:
+      name: DQN
+      action_pdtype: Argmax
+      action_policy: epsilon_greedy
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.084
+        start_step: 0
+        end_step: 30000
+      gamma: 0.985
+      training_batch_iter: 2
+      training_iter: 2
+      training_frequency: 1
+      training_start_step: 32
+    memory:
+      <<: *dqn_replay
+    net:
+      <<: *dqn_net_128x2_relu
+      optim_spec:
+        name: Adam
+        lr: 0.0006
+  env:
+    name: Acrobot-v1
+    max_t: null
+    max_frame: 300000
+    num_envs: 4
+  meta:
+    <<: *dqn_classic_meta
+    max_trial: 16
+    search_scheduler:
+      grace_period: 50000
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.98, 0.999]
+    agent.algorithm.explore_var_spec.end_val__uniform: [0.01, 0.1]
+    agent.net.optim_spec.lr__loguniform: [0.0003, 0.003]
+
+ddqn_per_acrobot_arc:
+  agent:
+    name: DoubleDQN
+    algorithm:
+      name: DoubleDQN
+      action_pdtype: Argmax
+      action_policy: epsilon_greedy
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.01
+        start_step: 0
+        end_step: 10000
+      gamma: 0.99
+      training_batch_iter: 2
+      training_iter: 2
+      training_frequency: 1
+      training_start_step: 32
+    memory:
+      <<: *dqn_per_replay
+    net:
+      <<: *dqn_net_128x2_relu
+  env:
+    name: Acrobot-v1
+    max_t: null
+    max_frame: 300000
+    num_envs: 4
+  meta:
+    <<: *dqn_classic_meta
+
+# -- MountainCar specs --
+
+dqn_mountaincar_arc:
+  agent:
+    name: DQN
+    algorithm:
+      name: DQN
+      action_pdtype: Argmax
+      action_policy: epsilon_greedy
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.01
+        start_step: 0
+        end_step: 50000
+      gamma: 0.99
+      training_batch_iter: 2
+      training_iter: 2
+      training_frequency: 4
+      training_start_step: 32
+    memory:
+      name: Replay
+      batch_size: 32
+      max_size: 50000
+      use_cer: true
+    net:
+      <<: *dqn_net_128x2_relu
+  env:
+    name: MountainCar-v0
+    max_t: null
+    max_frame: 500000
+  meta:
+    <<: *dqn_classic_meta
+    eval_frequency: 10000
diff --git a/slm_lab/spec/benchmark_arc/ppo/ppo_atari_arc.yaml b/slm_lab/spec/benchmark_arc/ppo/ppo_atari_arc.yaml
new file mode 100644
index 000000000..235085e43
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/ppo/ppo_atari_arc.yaml
@@ -0,0 +1,155 @@
+# PPO Atari - TorchArcNet specs
+# Covers: ppo_atari (lam95), ppo_atari_lam85, ppo_atari_lam70, ppo_atari_nosticky
+
+# -- Shared anchors --
+
+_ppo_atari_arc: &ppo_atari_arc
+  modules:
+    body:
+      Sequential:
+        - LazyConv2d: {out_channels: 32, kernel_size: 8, stride: 4, padding: 0}
+        - ReLU:
+        - LazyConv2d: {out_channels: 64, kernel_size: 4, stride: 2, padding: 0}
+        - ReLU:
+        - LazyConv2d: {out_channels: 64, kernel_size: 3, stride: 1, padding: 0}
+        - ReLU:
+        - Flatten:
+        - LazyLinear: {out_features: 512}
+        - ReLU:
+  graph:
+    input: x
+    modules:
+      body: [x]
+    output: body
+
+_ppo_atari_net: &ppo_atari_net
+  type: TorchArcNet
+  shared: true
+  arc: *ppo_atari_arc
+  hid_layers_activation: relu
+  init_fn: orthogonal_
+  actor_init_std: 0.01
+  critic_init_std: 1.0
+  clip_grad_val: 0.5
+  use_same_optim: true
+  loss_spec:
+    name: MSELoss
+  optim_spec: &atari_optim
+    name: AdamW
+    lr: 2.5e-4
+    eps: 1.0e-5
+  lr_scheduler_spec: &atari_lr_scheduler
+    name: LinearToZero
+    frame: 1.0e+7
+  normalize: true
+  gpu: auto
+
+_ppo_atari_algorithm: &ppo_atari_algorithm
+  name: PPO
+  action_pdtype: default
+  action_policy: default
+  explore_var_spec: null
+  gamma: 0.99
+  lam: 0.95
+  clip_eps_spec: &atari_clip
+    name: no_decay
+    start_val: 0.1
+    end_val: 0.1
+    start_step: 0
+    end_step: 0
+  entropy_coef_spec: &atari_entropy
+    name: no_decay
+    start_val: 0.01
+    end_val: 0.01
+    start_step: 0
+    end_step: 0
+  val_loss_coef: 0.5
+  time_horizon: 128
+  minibatch_size: 256
+  training_epoch: 4
+  clip_vloss: true
+
+_ppo_atari_env: &ppo_atari_env
+  name: "${env}"
+  num_envs: 16
+  max_frame: 1.0e+7
+  life_loss_info: true
+
+_ppo_atari_meta: &ppo_atari_meta
+  distributed: false
+  eval_frequency: 10000
+  log_frequency: 10000
+  max_session: 4
+  max_trial: 1
+
+# -- Specs --
+
+ppo_atari_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_atari_algorithm
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_atari_net
+  env:
+    <<: *ppo_atari_env
+  meta:
+    <<: *ppo_atari_meta
+    max_trial: 16
+    search_resources:
+      cpu: 2
+      gpu: 0.25
+    search_scheduler:
+      grace_period: 500000
+      reduction_factor: 3
+  search:
+    agent.algorithm.lam__uniform: [0.7, 0.98]
+    agent.algorithm.entropy_coef_spec.start_val__loguniform: [0.005, 0.03]
+    agent.net.optim_spec.lr__loguniform: [1.0e-4, 5.0e-4]
+
+ppo_atari_lam85_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_atari_algorithm
+      lam: 0.85
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_atari_net
+  env:
+    <<: *ppo_atari_env
+  meta:
+    <<: *ppo_atari_meta
+
+ppo_atari_lam70_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_atari_algorithm
+      lam: 0.70
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_atari_net
+  env:
+    <<: *ppo_atari_env
+  meta:
+    <<: *ppo_atari_meta
+
+ppo_atari_nosticky_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_atari_algorithm
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_atari_net
+  env:
+    <<: *ppo_atari_env
+    repeat_action_probability: 0.0
+  meta:
+    <<: *ppo_atari_meta
diff --git a/slm_lab/spec/benchmark_arc/ppo/ppo_box2d_arc.yaml b/slm_lab/spec/benchmark_arc/ppo/ppo_box2d_arc.yaml
new file mode 100644
index 000000000..91e35e7a7
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/ppo/ppo_box2d_arc.yaml
@@ -0,0 +1,248 @@
+# PPO Box2D - TorchArcNet specs
+# Covers: LunarLander-v3 (discrete), LunarLander-v3 (continuous), BipedalWalker-v3
+
+# -- Shared anchors --
+
+_ppo_box2d_algorithm: &ppo_box2d_algorithm
+  name: PPO
+  action_pdtype: default
+  action_policy: default
+  explore_var_spec: null
+  gamma: 0.99
+  lam: 0.95
+  clip_eps_spec:
+    name: no_decay
+    start_val: 0.2
+    end_val: 0.2
+    start_step: 0
+    end_step: 0
+  entropy_coef_spec:
+    name: no_decay
+    start_val: 0.0
+    end_val: 0.0
+    start_step: 0
+    end_step: 0
+  val_loss_coef: 0.5
+  clip_val_loss: false
+  time_horizon: 512
+  minibatch_size: 256
+  training_epoch: 15
+
+_ppo_box2d_meta: &ppo_box2d_meta
+  distributed: false
+  log_frequency: 1000
+  eval_frequency: 1024
+  max_session: 4
+  max_trial: 16
+  search_scheduler:
+    grace_period: 50000
+    reduction_factor: 3
+
+# -- Specs --
+
+ppo_lunar_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_box2d_algorithm
+      gamma: 0.9914
+      lam: 0.9527
+      clip_eps_spec:
+        name: linear_decay
+        start_val: 0.236
+        end_val: 0.2
+        start_step: 0
+        end_step: 300000
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.00001
+        end_val: 0.00001
+        start_step: 0
+        end_step: 0
+      val_loss_coef: 1.1395
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      shared: false
+      arc: &lunar_arc
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 128}
+              - Tanh:
+              - LazyLinear: {out_features: 128}
+              - Tanh:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      hid_layers_activation: tanh
+      init_fn: orthogonal_
+      actor_init_std: 0.01
+      critic_init_std: 1.0
+      clip_grad_val: 1.0
+      use_same_optim: false
+      loss_spec:
+        name: MSELoss
+      actor_optim_spec:
+        name: AdamW
+        lr: 0.000565
+      critic_optim_spec:
+        name: AdamW
+        lr: 0.00558
+      gpu: auto
+  env:
+    name: LunarLander-v3
+    max_t: null
+    max_frame: 300000
+    num_envs: 8
+  meta:
+    <<: *ppo_box2d_meta
+  search:
+    agent.algorithm.gamma__uniform: [0.98, 0.999]
+    agent.algorithm.lam__uniform: [0.9, 0.98]
+    agent.net.actor_optim_spec.lr__loguniform: [0.0001, 0.001]
+    agent.net.critic_optim_spec.lr__loguniform: [0.001, 0.01]
+
+ppo_lunar_continuous_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_box2d_algorithm
+      gamma: 0.9858
+      lam: 0.9758
+      clip_eps_spec:
+        name: linear_decay
+        start_val: 0.128
+        end_val: 0.2
+        start_step: 0
+        end_step: 300000
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.0000127
+        end_val: 0.0000127
+        start_step: 0
+        end_step: 0
+      val_loss_coef: 1.452
+      time_horizon: 640
+      minibatch_size: 128
+      training_epoch: 8
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      shared: false
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 128}
+              - ReLU:
+              - LazyLinear: {out_features: 64}
+              - ReLU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      hid_layers_activation: relu
+      init_fn: orthogonal_
+      actor_init_std: 0.01
+      critic_init_std: 1.0
+      clip_grad_val: 1.0
+      use_same_optim: false
+      loss_spec:
+        name: MSELoss
+      actor_optim_spec:
+        name: AdamW
+        lr: 0.000267
+      critic_optim_spec:
+        name: AdamW
+        lr: 0.00134
+      gpu: auto
+  env:
+    name: LunarLander-v3
+    continuous: true
+    max_t: null
+    max_frame: 300000
+    num_envs: 8
+  meta:
+    <<: *ppo_box2d_meta
+  search:
+    agent.algorithm.gamma__uniform: [0.975, 0.99]
+    agent.algorithm.lam__uniform: [0.96, 0.98]
+    agent.net.actor_optim_spec.lr__loguniform: [0.0001, 0.0003]
+    agent.net.critic_optim_spec.lr__loguniform: [0.0005, 0.002]
+
+ppo_bipedalwalker_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_box2d_algorithm
+      gamma: 0.999
+      lam: 0.95
+      clip_eps_spec:
+        name: no_decay
+        start_val: 0.18
+        end_val: 0.18
+        start_step: 0
+        end_step: 0
+      time_horizon: 2048
+      minibatch_size: 64
+      training_epoch: 10
+      normalize_v_targets: true
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      shared: false
+      arc: &bipedal_arc
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 256}
+              - Tanh:
+              - LazyLinear: {out_features: 256}
+              - Tanh:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      hid_layers_activation: tanh
+      init_fn: orthogonal_
+      actor_init_std: 0.01
+      critic_init_std: 1.0
+      clip_grad_val: 0.5
+      use_same_optim: true
+      loss_spec:
+        name: MSELoss
+      optim_spec:
+        name: AdamW
+        lr: 0.0003
+      gpu: auto
+  env:
+    name: BipedalWalker-v3
+    num_envs: 32
+    max_t: null
+    max_frame: 1.0e+6
+    normalize_obs: true
+    normalize_reward: true
+  meta:
+    distributed: false
+    log_frequency: 10000
+    eval_frequency: 10000
+    max_session: 4
+    max_trial: 12
+    search_resources:
+      cpu: 1
+      gpu: 0.125
+    search_scheduler:
+      grace_period: 500000
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.995, 0.9999]
+    agent.algorithm.lam__uniform: [0.93, 0.97]
+    agent.net.optim_spec.lr__loguniform: [0.0002, 0.0005]
diff --git a/slm_lab/spec/benchmark_arc/ppo/ppo_classic_arc.yaml b/slm_lab/spec/benchmark_arc/ppo/ppo_classic_arc.yaml
new file mode 100644
index 000000000..b069424f4
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/ppo/ppo_classic_arc.yaml
@@ -0,0 +1,192 @@
+# PPO Classic Control - TorchArcNet specs
+# Covers: CartPole-v1, Acrobot-v1, Pendulum-v1
+
+# -- Shared anchors --
+
+_ppo_classic_meta: &ppo_classic_meta
+  distributed: false
+  log_frequency: 500
+  eval_frequency: 500
+  max_session: 4
+  max_trial: 1
+
+_ppo_classic_net: &ppo_classic_net
+  type: TorchArcNet
+  shared: false
+  arc:
+    modules:
+      body:
+        Sequential:
+          - LazyLinear: {out_features: 64}
+          - Tanh:
+          - LazyLinear: {out_features: 64}
+          - Tanh:
+    graph:
+      input: x
+      modules:
+        body: [x]
+      output: body
+  hid_layers_activation: tanh
+  clip_grad_val: 0.5
+  use_same_optim: false
+  loss_spec:
+    name: MSELoss
+  gpu: auto
+
+_ppo_classic_algorithm: &ppo_classic_algorithm
+  name: PPO
+  action_pdtype: default
+  action_policy: default
+  explore_var_spec: null
+  gamma: 0.99
+  lam: 0.95
+  clip_eps_spec:
+    name: no_decay
+    start_val: 0.2
+    end_val: 0.2
+    start_step: 0
+    end_step: 0
+  entropy_coef_spec:
+    name: no_decay
+    start_val: 0.01
+    end_val: 0.01
+    start_step: 0
+    end_step: 0
+  val_loss_coef: 0.5
+  clip_val_loss: false
+  time_horizon: 256
+  minibatch_size: 64
+  training_epoch: 10
+
+# -- Specs --
+
+ppo_cartpole_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_classic_algorithm
+      gamma: 0.9769
+      lam: 0.9112
+      clip_eps_spec:
+        name: no_decay
+        start_val: 0.1312
+        end_val: 0.1312
+        start_step: 0
+        end_step: 200000
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.0074
+        end_val: 0.0074
+        start_step: 0
+        end_step: 200000
+      val_loss_coef: 0.6049
+      time_horizon: 256
+      minibatch_size: 128
+      training_epoch: 15
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_classic_net
+      actor_optim_spec:
+        name: Adam
+        lr: 0.0005909
+      critic_optim_spec:
+        name: Adam
+        lr: 0.0006352
+  env:
+    name: CartPole-v1
+    max_t: null
+    max_frame: 200000
+    num_envs: 4
+  meta:
+    <<: *ppo_classic_meta
+    eval_frequency: 256
+
+ppo_acrobot_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_classic_algorithm
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_classic_net
+      actor_optim_spec:
+        name: Adam
+        lr: 0.0003
+      critic_optim_spec:
+        name: Adam
+        lr: 0.001
+  env:
+    name: Acrobot-v1
+    max_t: null
+    max_frame: 300000
+    num_envs: 4
+  meta:
+    <<: *ppo_classic_meta
+
+ppo_pendulum_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_classic_algorithm
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.0
+        end_val: 0.0
+        start_step: 0
+        end_step: 0
+      time_horizon: 2048
+      minibatch_size: 64
+      training_epoch: 10
+      normalize_v_targets: true
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      shared: false
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 256}
+              - ReLU:
+              - LazyLinear: {out_features: 256}
+              - ReLU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      hid_layers_activation: relu
+      init_fn: orthogonal_
+      actor_init_std: 0.01
+      critic_init_std: 1.0
+      clip_grad_val: 0.5
+      use_same_optim: true
+      loss_spec:
+        name: MSELoss
+      optim_spec:
+        name: Adam
+        lr: 3.0e-4
+      gpu: auto
+  env:
+    name: Pendulum-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 300000
+    normalize_obs: false
+    normalize_reward: false
+  meta:
+    <<: *ppo_classic_meta
+    rigorous_eval: 0
+    max_trial: 16
+    search_resources:
+      cpu: 1
+      gpu: 0.125
+    search_scheduler:
+      grace_period: 10000
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.95, 0.995]
+    agent.net.optim_spec.lr__loguniform: [1.0e-4, 1.0e-3]
diff --git a/slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml b/slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml
new file mode 100644
index 000000000..d2c5116c6
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/ppo/ppo_mujoco_arc.yaml
@@ -0,0 +1,393 @@
+# PPO MuJoCo - TorchArcNet specs
+# Covers: generic mujoco, longhorizon, ant, hopper, swimmer,
+#   inverted_pendulum, inverted_double_pendulum, pusher, mountaincar
+
+# -- Shared anchors --
+
+_ppo_mujoco_arc: &ppo_mujoco_arc
+  modules:
+    body:
+      Sequential:
+        - LazyLinear: {out_features: 256}
+        - Tanh:
+        - LazyLinear: {out_features: 256}
+        - Tanh:
+  graph:
+    input: x
+    modules:
+      body: [x]
+    output: body
+
+_ppo_mujoco_net: &ppo_mujoco_net
+  type: TorchArcNet
+  shared: false
+  arc: *ppo_mujoco_arc
+  hid_layers_activation: tanh
+  init_fn: orthogonal_
+  actor_init_std: 0.01
+  critic_init_std: 1.0
+  clip_grad_val: 0.5
+  use_same_optim: true
+  log_std_init: 0.0
+  loss_spec:
+    name: MSELoss
+  optim_spec: &mujoco_optim
+    name: AdamW
+    lr: 3.0e-4
+  gpu: auto
+
+_ppo_mujoco_algorithm: &ppo_mujoco_algorithm
+  name: PPO
+  action_pdtype: default
+  action_policy: default
+  explore_var_spec: null
+  gamma: 0.99
+  lam: 0.95
+  clip_eps_spec: &clip_02
+    name: no_decay
+    start_val: 0.2
+    end_val: 0.2
+    start_step: 0
+    end_step: 0
+  entropy_coef_spec: &entropy_0
+    name: no_decay
+    start_val: 0.0
+    end_val: 0.0
+    start_step: 0
+    end_step: 0
+  val_loss_coef: 0.5
+  time_horizon: 2048
+  minibatch_size: 64
+  training_epoch: 10
+  normalize_v_targets: true
+
+_ppo_mujoco_env: &ppo_mujoco_env
+  name: "${env}"
+  num_envs: 16
+  max_t: null
+  max_frame: "${max_frame}"
+  normalize_obs: true
+  normalize_reward: true
+
+_ppo_mujoco_meta: &ppo_mujoco_meta
+  distributed: false
+  log_frequency: 10000
+  eval_frequency: 10000
+  rigorous_eval: 0
+  max_session: 4
+  max_trial: 1
+
+_ppo_mujoco_search_meta: &ppo_mujoco_search_meta
+  distributed: false
+  log_frequency: 10000
+  eval_frequency: 1000
+  rigorous_eval: 0
+  max_session: 4
+  max_trial: 16
+  search_resources:
+    cpu: 1
+    gpu: 0.125
+  search_scheduler:
+    grace_period: 2.0e+5
+    reduction_factor: 3
+
+# -- Generic specs (use with -s env=X -s max_frame=Y) --
+
+ppo_mujoco_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_mujoco_algorithm
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_mujoco_net
+  env:
+    <<: *ppo_mujoco_env
+  meta:
+    <<: *ppo_mujoco_meta
+
+ppo_mujoco_longhorizon_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_mujoco_algorithm
+      gamma: 0.997
+      lam: 0.97
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.001
+        end_val: 0.001
+        start_step: 0
+        end_step: 0
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_mujoco_net
+      optim_spec:
+        name: AdamW
+        lr: 2.0e-4
+  env:
+    <<: *ppo_mujoco_env
+  meta:
+    <<: *ppo_mujoco_meta
+
+# -- Per-environment specs --
+
+ppo_ant_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_mujoco_algorithm
+      gamma: 0.988
+      lam: 0.928
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_mujoco_net
+      optim_spec:
+        name: AdamW
+        lr: 1.5e-4
+  env:
+    <<: *ppo_mujoco_env
+    name: Ant-v5
+    max_frame: "${max_frame}"
+  meta:
+    <<: *ppo_mujoco_search_meta
+  search:
+    agent.algorithm.gamma__uniform: [0.98, 0.999]
+    agent.algorithm.lam__uniform: [0.92, 0.98]
+    agent.net.optim_spec.lr__loguniform: [1.0e-4, 5.0e-4]
+
+ppo_hopper_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_mujoco_algorithm
+      gamma: 0.991
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.005
+        end_val: 0.005
+        start_step: 0
+        end_step: 0
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_mujoco_net
+      lr_scheduler_spec:
+        name: LinearToZero
+        frame: "${max_frame}"
+  env:
+    <<: *ppo_mujoco_env
+    name: Hopper-v5
+    max_frame: "${max_frame}"
+  meta:
+    <<: *ppo_mujoco_search_meta
+  search:
+    agent.algorithm.gamma__uniform: [0.98, 0.999]
+    agent.algorithm.lam__uniform: [0.9, 0.98]
+    agent.net.optim_spec.lr__loguniform: [1.0e-4, 1.0e-3]
+
+ppo_swimmer_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_mujoco_algorithm
+      gamma: 0.997
+      lam: 0.968
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.025
+        end_val: 0.025
+        start_step: 0
+        end_step: 0
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_mujoco_net
+      optim_spec:
+        name: AdamW
+        lr: 2.2e-4
+  env:
+    <<: *ppo_mujoco_env
+    name: Swimmer-v5
+    max_frame: "${max_frame}"
+  meta:
+    <<: *ppo_mujoco_search_meta
+    search_scheduler:
+      grace_period: 1.0e+5
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.98, 0.999]
+    agent.algorithm.lam__uniform: [0.9, 0.98]
+    agent.net.optim_spec.lr__loguniform: [1.0e-4, 1.0e-3]
+
+ppo_inverted_pendulum_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_mujoco_algorithm
+      gamma: 0.999
+      lam: 0.9
+      clip_eps_spec:
+        name: no_decay
+        start_val: 0.4
+        end_val: 0.4
+        start_step: 0
+        end_step: 0
+      val_loss_coef: 0.2
+      time_horizon: 32
+      minibatch_size: 32
+      training_epoch: 5
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_mujoco_net
+      optim_spec:
+        name: AdamW
+        lr: 5.7e-4
+      lr_scheduler_spec:
+        name: LinearToZero
+        frame: "${max_frame}"
+  env:
+    <<: *ppo_mujoco_env
+    name: InvertedPendulum-v5
+    max_frame: "${max_frame}"
+  meta:
+    <<: *ppo_mujoco_search_meta
+    search_scheduler:
+      grace_period: 1.0e+5
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.98, 0.999]
+    agent.algorithm.lam__uniform: [0.88, 0.95]
+    agent.net.optim_spec.lr__loguniform: [3.0e-4, 8.0e-4]
+
+ppo_inverted_double_pendulum_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_mujoco_algorithm
+      gamma: 0.98
+      lam: 0.8
+      clip_eps_spec:
+        name: no_decay
+        start_val: 0.4
+        end_val: 0.4
+        start_step: 0
+        end_step: 0
+      val_loss_coef: 0.7
+      time_horizon: 128
+      minibatch_size: 128
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_mujoco_net
+      optim_spec:
+        name: AdamW
+        lr: 1.55e-4
+      lr_scheduler_spec:
+        name: LinearToZero
+        frame: "${max_frame}"
+  env:
+    <<: *ppo_mujoco_env
+    name: InvertedDoublePendulum-v5
+    max_frame: "${max_frame}"
+  meta:
+    <<: *ppo_mujoco_search_meta
+    search_scheduler:
+      grace_period: 1.0e+5
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.96, 0.99]
+    agent.algorithm.lam__uniform: [0.7, 0.9]
+    agent.net.optim_spec.lr__loguniform: [1.0e-4, 5.0e-4]
+
+ppo_pusher_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_mujoco_algorithm
+      gamma: 0.982
+      lam: 0.927
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *ppo_mujoco_net
+      optim_spec:
+        name: AdamW
+        lr: 1.5e-4
+  env:
+    <<: *ppo_mujoco_env
+    name: Pusher-v5
+    max_frame: "${max_frame}"
+  meta:
+    <<: *ppo_mujoco_meta
+
+ppo_mountaincar_arc:
+  agent:
+    name: PPO
+    algorithm:
+      <<: *ppo_mujoco_algorithm
+      lam: 0.98
+      clip_eps_spec:
+        name: no_decay
+        start_val: 0.2
+        end_val: 0.2
+        start_step: 0
+        end_step: 500000
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.0
+        end_val: 0.0
+        start_step: 0
+        end_step: 500000
+      clip_val_loss: false
+      time_horizon: 32
+      minibatch_size: 256
+      normalize_v_targets: false
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      shared: false
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 256}
+              - ReLU:
+              - LazyLinear: {out_features: 256}
+              - ReLU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      hid_layers_activation: relu
+      init_fn: orthogonal_
+      actor_init_std: 0.01
+      critic_init_std: 1.0
+      clip_grad_val: 0.5
+      use_same_optim: false
+      loss_spec:
+        name: MSELoss
+      actor_optim_spec:
+        name: Adam
+        lr: 0.0001
+      critic_optim_spec:
+        name: Adam
+        lr: 0.0001
+      gpu: auto
+  env:
+    name: MountainCar-v0
+    max_t: null
+    max_frame: 1000000
+    num_envs: 16
+  meta:
+    distributed: false
+    log_frequency: 1000
+    eval_frequency: 10000
+    max_session: 4
+    max_trial: 1
diff --git a/slm_lab/spec/benchmark_arc/reinforce/reinforce_arc.yaml b/slm_lab/spec/benchmark_arc/reinforce/reinforce_arc.yaml
new file mode 100644
index 000000000..634734269
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/reinforce/reinforce_arc.yaml
@@ -0,0 +1,265 @@
+# REINFORCE Classic Control - TorchArcNet specs
+# Covers: CartPole-v1 (multiple variants)
+
+# -- Shared anchors --
+
+_reinforce_meta: &reinforce_meta
+  distributed: false
+  eval_frequency: 500
+  max_session: 4
+  max_trial: 1
+  log_frequency: 500
+
+_reinforce_algorithm: &reinforce_algorithm
+  name: Reinforce
+  action_pdtype: default
+  action_policy: default
+  center_return: true
+  explore_var_spec: null
+  gamma: 0.99
+  entropy_coef_spec: &entropy_linear_decay_001
+    name: linear_decay
+    start_val: 0.001
+    end_val: 0.0
+    start_step: 0
+    end_step: 200000
+  training_frequency: 512
+
+_reinforce_net_128x2_tanh: &reinforce_net_128x2_tanh
+  type: TorchArcNet
+  arc:
+    modules:
+      body:
+        Sequential:
+          - LazyLinear: {out_features: 128}
+          - Tanh:
+          - LazyLinear: {out_features: 128}
+          - Tanh:
+    graph:
+      input: x
+      modules:
+        body: [x]
+      output: body
+  hid_layers_activation: tanh
+  init_fn: orthogonal_
+  clip_grad_val: 0.5
+  loss_spec:
+    name: MSELoss
+  optim_spec:
+    name: Adam
+    lr: 0.002
+
+# -- Specs --
+
+reinforce_cartpole_arc:
+  agent:
+    name: Reinforce
+    algorithm:
+      <<: *reinforce_algorithm
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *reinforce_net_128x2_tanh
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta:
+    <<: *reinforce_meta
+
+reinforce_baseline_cartpole_arc:
+  agent:
+    name: Reinforce
+    algorithm:
+      <<: *reinforce_algorithm
+      entropy_coef_spec:
+        name: linear_decay
+        start_val: 0.01
+        end_val: 0.001
+        start_step: 0
+        end_step: 20000
+      training_frequency: 128
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 64}
+              - SELU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      clip_grad_val: null
+      loss_spec:
+        name: MSELoss
+      optim_spec:
+        name: AdamW
+        lr: 0.002
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 150000
+  meta:
+    <<: *reinforce_meta
+
+reinforce_cartpole_extended_arc:
+  agent:
+    name: Reinforce
+    algorithm:
+      <<: *reinforce_algorithm
+      gamma: 0.96
+      entropy_coef_spec:
+        name: linear_decay
+        start_val: 0.005
+        end_val: 0.0
+        start_step: 0
+        end_step: 250000
+      training_frequency: 256
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *reinforce_net_128x2_tanh
+      optim_spec:
+        name: Adam
+        lr: 0.0017
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 250000
+  meta:
+    <<: *reinforce_meta
+
+reinforce_cartpole_boosted_arc:
+  agent:
+    name: Reinforce
+    algorithm:
+      <<: *reinforce_algorithm
+      gamma: 0.965
+      entropy_coef_spec:
+        name: linear_decay
+        start_val: 0.006
+        end_val: 0.0
+        start_step: 0
+        end_step: 150000
+      training_frequency: 256
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 256}
+              - Tanh:
+              - LazyLinear: {out_features: 256}
+              - Tanh:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      hid_layers_activation: tanh
+      init_fn: orthogonal_
+      clip_grad_val: 0.5
+      loss_spec:
+        name: MSELoss
+      optim_spec:
+        name: Adam
+        lr: 0.0015
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta:
+    <<: *reinforce_meta
+
+reinforce_cartpole_asha_best_arc:
+  agent:
+    name: Reinforce
+    algorithm:
+      <<: *reinforce_algorithm
+      gamma: 0.9791
+      entropy_coef_spec:
+        name: linear_decay
+        start_val: 0.000658
+        end_val: 0.0
+        start_step: 0
+        end_step: 200000
+      training_frequency: 512
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *reinforce_net_128x2_tanh
+      optim_spec:
+        name: Adam
+        lr: 0.00196
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta:
+    <<: *reinforce_meta
+    max_session: 8
+
+reinforce_cartpole_mega_search_arc:
+  agent:
+    name: Reinforce
+    algorithm:
+      <<: *reinforce_algorithm
+      gamma: 0.97
+      entropy_coef_spec:
+        name: linear_decay
+        start_val: 0.003
+        end_val: 0.0
+        start_step: 0
+        end_step: 120000
+      training_frequency: 256
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear: {out_features: 256}
+              - Tanh:
+              - LazyLinear: {out_features: 256}
+              - Tanh:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      hid_layers_activation: tanh
+      init_fn: orthogonal_
+      clip_grad_val: 0.5
+      loss_spec:
+        name: MSELoss
+      optim_spec:
+        name: Adam
+        lr: 0.002
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta:
+    <<: *reinforce_meta
+    log_frequency: 2000
+    max_trial: 12
+  search:
+    agent.algorithm.gamma__choice: [0.96, 0.97, 0.98]
+    agent.algorithm.entropy_coef_spec.start_val__choice: [0.002, 0.003, 0.004]
+    agent.net.optim_spec.lr__choice: [0.0015, 0.002, 0.0025]
diff --git a/slm_lab/spec/benchmark_arc/sac/sac_atari_arc.yaml b/slm_lab/spec/benchmark_arc/sac/sac_atari_arc.yaml
new file mode 100644
index 000000000..b9de6ab27
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/sac/sac_atari_arc.yaml
@@ -0,0 +1,79 @@
+# SAC Atari using TorchArcNet
+# ConvNet body: LazyConv2d(32,8,4) + ReLU + LazyConv2d(64,4,2) + ReLU +
+#   LazyConv2d(64,3,1) + ReLU + Flatten + LazyLinear(512) + ReLU
+# normalize=true in net spec normalizes pixel inputs to [0,1] on GPU in forward pass.
+
+sac_atari_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: Categorical
+      action_policy: default
+      gamma: 0.99
+      training_start_step: 1000
+      training_frequency: 4
+      training_iter: 3
+    memory:
+      name: Replay
+      batch_size: 256
+      max_size: 200000
+      use_cer: false
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyConv2d:
+                  out_channels: 32
+                  kernel_size: 8
+                  stride: 4
+              - ReLU:
+              - LazyConv2d:
+                  out_channels: 64
+                  kernel_size: 4
+                  stride: 2
+              - ReLU:
+              - LazyConv2d:
+                  out_channels: 64
+                  kernel_size: 3
+                  stride: 1
+              - ReLU:
+              - Flatten:
+              - LazyLinear:
+                  out_features: 512
+              - ReLU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      shared: false
+      hid_layers_activation: relu
+      init_fn: orthogonal_
+      clip_grad_val: 0.5
+      use_same_optim: false
+      loss_spec:
+        name: SmoothL1Loss
+      optim_spec:
+        name: AdamW
+        lr: 0.0003
+        eps: 0.0001
+      update_type: polyak
+      update_frequency: 1
+      polyak_coef: 0.005
+      normalize: true
+      gpu: auto
+  env:
+    name: ${env}
+    num_envs: 16
+    max_t: null
+    max_frame: 2000000
+    life_loss_info: true
+  meta:
+    distributed: false
+    eval_frequency: 10000
+    log_frequency: 10000
+    max_session: 4
+    max_trial: 1
diff --git a/slm_lab/spec/benchmark_arc/sac/sac_box2d_arc.yaml b/slm_lab/spec/benchmark_arc/sac/sac_box2d_arc.yaml
new file mode 100644
index 000000000..16e044e0f
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/sac/sac_box2d_arc.yaml
@@ -0,0 +1,142 @@
+# SAC Box2D using TorchArcNet
+# Covers: LunarLander-v3 (discrete), LunarLanderContinuous-v3, BipedalWalker-v3
+
+# --- Shared anchors ---
+
+_arc_body: &arc_body
+  modules:
+    body:
+      Sequential:
+        - LazyLinear:
+            out_features: 256
+        - ReLU:
+        - LazyLinear:
+            out_features: 256
+        - ReLU:
+  graph:
+    input: x
+    modules:
+      body: [x]
+    output: body
+
+_sac_net: &sac_net
+  type: TorchArcNet
+  arc: *arc_body
+  hid_layers_activation: relu
+  clip_grad_val: null
+  loss_spec:
+    name: MSELoss
+  optim_spec:
+    name: Adam
+    lr: 0.0003
+  update_type: polyak
+  polyak_coef: 0.005
+  gpu: auto
+
+_memory: &memory
+  name: Replay
+  batch_size: 256
+  max_size: 100000
+  use_cer: false
+
+_meta: &meta
+  distributed: false
+  log_frequency: 1000
+  eval_frequency: 5000
+  max_session: 4
+  max_trial: 1
+
+# --- Specs ---
+
+sac_lunar_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: Categorical
+      action_policy: default
+      gamma: 0.99
+      training_frequency: 1
+      training_start_step: 1000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: LunarLander-v3
+    num_envs: 8
+    max_t: null
+    max_frame: 300000
+  meta: *meta
+
+sac_lunar_continuous_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: Normal
+      action_policy: default
+      gamma: 0.994
+      training_iter: 4
+      training_frequency: 1
+      training_start_step: 256
+    memory:
+      <<: *memory
+      max_size: 1000000
+    net:
+      <<: *sac_net
+      optim_spec:
+        name: Adam
+        lr: 1.17e-4
+  env:
+    name: LunarLanderContinuous-v3
+    num_envs: 8
+    max_t: null
+    max_frame: 300000
+  meta: *meta
+
+sac_bipedalwalker_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: default
+      action_policy: default
+      gamma: 0.99
+      training_frequency: 20
+      training_iter: 20
+      training_start_step: 1000
+    memory:
+      name: Replay
+      batch_size: 512
+      max_size: 1000000
+      use_cer: true
+    net:
+      <<: *sac_net
+      init_fn: orthogonal_
+      clip_grad_val: 1.0
+      optim_spec:
+        name: AdamW
+        lr: 0.0003
+      update_frequency: 1
+  env:
+    name: BipedalWalker-v3
+    num_envs: 16
+    max_t: null
+    max_frame: 1000000
+  meta:
+    distributed: false
+    log_frequency: 10000
+    eval_frequency: 10000
+    rigorous_eval: 0
+    max_session: 4
+    max_trial: 12
+    search_resources:
+      cpu: 1
+      gpu: 0.125
+    search_scheduler:
+      grace_period: 100000
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.98, 0.999]
+    agent.algorithm.training_start_step__qrandint: [500, 2000, 500]
+    agent.net.optim_spec.lr__loguniform: [0.0001, 0.001]
+    agent.net.polyak_coef__loguniform: [0.001, 0.02]
diff --git a/slm_lab/spec/benchmark_arc/sac/sac_classic_arc.yaml b/slm_lab/spec/benchmark_arc/sac/sac_classic_arc.yaml
new file mode 100644
index 000000000..29220ba19
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/sac/sac_classic_arc.yaml
@@ -0,0 +1,111 @@
+# SAC Classic Control using TorchArcNet
+# Covers: CartPole-v1, Acrobot-v1 (discrete), Pendulum-v1 (continuous)
+
+# --- Shared anchors ---
+
+_arc_body: &arc_body
+  modules:
+    body:
+      Sequential:
+        - LazyLinear:
+            out_features: 256
+        - ReLU:
+        - LazyLinear:
+            out_features: 256
+        - ReLU:
+  graph:
+    input: x
+    modules:
+      body: [x]
+    output: body
+
+_sac_net: &sac_net
+  type: TorchArcNet
+  arc: *arc_body
+  hid_layers_activation: relu
+  clip_grad_val: null
+  loss_spec:
+    name: MSELoss
+  optim_spec:
+    name: Adam
+    lr: 0.0003
+  update_type: polyak
+  polyak_coef: 0.005
+  gpu: auto
+
+_discrete_algorithm: &discrete_algorithm
+  name: SoftActorCritic
+  action_pdtype: Categorical
+  action_policy: default
+  gamma: 0.99
+  training_frequency: 1
+  training_start_step: 1000
+
+_memory: &memory
+  name: Replay
+  batch_size: 256
+  max_size: 100000
+  use_cer: false
+
+_meta: &meta
+  distributed: false
+  log_frequency: 500
+  eval_frequency: 500
+  max_session: 4
+  max_trial: 1
+
+# --- Specs ---
+
+sac_cartpole_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm: *discrete_algorithm
+    memory: *memory
+    net: *sac_net
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta: *meta
+
+sac_acrobot_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm: *discrete_algorithm
+    memory: *memory
+    net:
+      <<: *sac_net
+      clip_grad_val: 0.5
+      loss_spec:
+        name: SmoothL1Loss
+  env:
+    name: Acrobot-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 300000
+  meta:
+    <<: *meta
+    eval_frequency: 5000
+
+sac_pendulum_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: Normal
+      action_policy: default
+      gamma: 0.99
+      training_iter: 4
+      training_frequency: 1
+      training_start_step: 256
+    memory: *memory
+    net: *sac_net
+  env:
+    name: Pendulum-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 300000
+  meta:
+    <<: *meta
+    eval_frequency: 5000
diff --git a/slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml b/slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml
new file mode 100644
index 000000000..b02ea2a50
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/sac/sac_mujoco_arc.yaml
@@ -0,0 +1,339 @@
+# SAC MuJoCo using TorchArcNet
+# Env-dependent hyperparams: training_iter, gamma, lr, polyak_coef, hidden layers, buffer size
+# Base config uses standard SAC defaults; each env overrides its tuned params.
+
+# --- Shared anchors ---
+
+_arc_body_256: &arc_body_256
+  modules:
+    body:
+      Sequential:
+        - LazyLinear:
+            out_features: 256
+        - ReLU:
+        - LazyLinear:
+            out_features: 256
+        - ReLU:
+  graph:
+    input: x
+    modules:
+      body: [x]
+    output: body
+
+_arc_body_400_300: &arc_body_400_300
+  modules:
+    body:
+      Sequential:
+        - LazyLinear:
+            out_features: 400
+        - ReLU:
+        - LazyLinear:
+            out_features: 300
+        - ReLU:
+  graph:
+    input: x
+    modules:
+      body: [x]
+    output: body
+
+_sac_net: &sac_net
+  type: TorchArcNet
+  arc: *arc_body_256
+  hid_layers_activation: relu
+  init_fn: orthogonal_
+  clip_grad_val: null
+  loss_spec:
+    name: MSELoss
+  optim_spec:
+    name: Adam
+    lr: 0.0003
+  update_type: polyak
+  update_frequency: 1
+  polyak_coef: 0.005
+  gpu: auto
+
+_sac_algorithm: &sac_algorithm
+  name: SoftActorCritic
+  action_pdtype: default
+  action_policy: default
+  gamma: 0.99
+  training_frequency: 1
+  training_iter: 4
+  training_start_step: 5000
+
+_memory: &memory
+  name: Replay
+  batch_size: 256
+  max_size: 1000000
+  use_cer: false
+
+_meta: &meta
+  distributed: false
+  log_frequency: 10000
+  eval_frequency: 10000
+  rigorous_eval: 0
+  max_session: 4
+  max_trial: 1
+
+# --- Generic MuJoCo spec (with env/max_frame substitution) ---
+
+sac_mujoco_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm: *sac_algorithm
+    memory: *memory
+    net: *sac_net
+  env:
+    name: ${env}
+    num_envs: 16
+    max_t: null
+    max_frame: ${max_frame}
+  meta: *meta
+
+# --- Easy envs: training_iter=4, standard params ---
+
+sac_halfcheetah_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      <<: *sac_algorithm
+      training_iter: 2
+      training_start_step: 5000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: HalfCheetah-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 4000000
+  meta: *meta
+
+sac_inverted_pendulum_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm: *sac_algorithm
+    memory: *memory
+    net: *sac_net
+  env:
+    name: InvertedPendulum-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 2000000
+  meta:
+    <<: *meta
+    eval_frequency: 1000
+
+sac_inverted_double_pendulum_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      <<: *sac_algorithm
+      training_start_step: 1000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: InvertedDoublePendulum-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 2000000
+  meta:
+    <<: *meta
+    eval_frequency: 1000
+
+sac_swimmer_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      <<: *sac_algorithm
+      gamma: 0.9999
+      training_iter: 4
+      training_start_step: 1000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: Swimmer-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 2000000
+  meta:
+    <<: *meta
+    eval_frequency: 1000
+
+sac_reacher_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      <<: *sac_algorithm
+      training_iter: 4
+      training_start_step: 1000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: Reacher-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 1000000
+  meta:
+    <<: *meta
+    eval_frequency: 1000
+
+sac_pusher_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      <<: *sac_algorithm
+      training_iter: 4
+      training_start_step: 1000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: Pusher-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 1000000
+  meta:
+    <<: *meta
+    eval_frequency: 1000
+
+# --- Hard envs: iter=4, 3M frames (data>gradients: 75% compute, +16-71% score, 2x faster) ---
+
+sac_hopper_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      <<: *sac_algorithm
+      training_start_step: 10000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: Hopper-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 3000000
+  meta: *meta
+
+sac_walker2d_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      <<: *sac_algorithm
+      training_start_step: 10000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: Walker2d-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 3000000
+  meta: *meta
+
+# --- SB3-param envs: gamma=0.98, lr=7.3e-4, polyak=0.02, [400,300], buffer=300K, iter=16 ---
+
+sac_ant_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      <<: *sac_algorithm
+      gamma: 0.98
+      training_iter: 4
+      training_start_step: 10000
+    memory:
+      <<: *memory
+      max_size: 300000
+    net:
+      <<: *sac_net
+      arc: *arc_body_400_300
+      optim_spec:
+        name: Adam
+        lr: 7.3e-4
+      polyak_coef: 0.02
+  env:
+    name: Ant-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 2000000
+  meta: *meta
+
+sac_humanoid_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      <<: *sac_algorithm
+      gamma: 0.98
+      training_iter: 16
+      training_start_step: 10000
+    memory:
+      <<: *memory
+      max_size: 300000
+    net:
+      <<: *sac_net
+      arc: *arc_body_400_300
+      optim_spec:
+        name: Adam
+        lr: 7.3e-4
+      polyak_coef: 0.02
+  env:
+    name: Humanoid-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 1000000
+  meta:
+    <<: *meta
+    eval_frequency: 1000
+
+# --- Experiment: freq=4 test (same iter, 4x less training, 2x more data, ~4h) ---
+
+sac_humanoid_freq4_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      <<: *sac_algorithm
+      gamma: 0.98
+      training_frequency: 4
+      training_iter: 8
+      training_start_step: 10000
+    memory:
+      <<: *memory
+      max_size: 300000
+    net:
+      <<: *sac_net
+      arc: *arc_body_400_300
+      optim_spec:
+        name: Adam
+        lr: 7.3e-4
+      polyak_coef: 0.02
+  env:
+    name: Humanoid-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 4000000
+  meta:
+    <<: *meta
+    eval_frequency: 1000
+
+sac_humanoid_standup_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      <<: *sac_algorithm
+      gamma: 0.98
+      training_iter: 16
+      training_start_step: 10000
+    memory:
+      <<: *memory
+      max_size: 300000
+    net:
+      <<: *sac_net
+      arc: *arc_body_400_300
+      optim_spec:
+        name: Adam
+        lr: 7.3e-4
+      polyak_coef: 0.02
+  env:
+    name: HumanoidStandup-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 1000000
+  meta:
+    <<: *meta
+    eval_frequency: 1000
diff --git a/slm_lab/spec/benchmark_arc/sac/sac_mujoco_exp_arc.yaml b/slm_lab/spec/benchmark_arc/sac/sac_mujoco_exp_arc.yaml
new file mode 100644
index 000000000..3657b9d97
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/sac/sac_mujoco_exp_arc.yaml
@@ -0,0 +1,180 @@
+# SAC MuJoCo experimental specs using TorchArcNet
+# Testing: fewer gradients + more data for better speed/perf tradeoff
+# Same analysis as SAC Atari experiments (compute budget optimization)
+#
+# Compute budget formula: total_grads = (max_frame / (num_envs * freq)) * iter
+# Current Hopper/Walker: iter=8, 2M frames → 1M grads, ~118fps, ~4.7h
+# Current easy envs: iter=4, 2M frames → 500K grads, ~200fps, ~2.8h
+
+# --- Shared anchors ---
+
+_arc_body_256: &arc_body_256
+  modules:
+    body:
+      Sequential:
+        - LazyLinear:
+            out_features: 256
+        - ReLU:
+        - LazyLinear:
+            out_features: 256
+        - ReLU:
+  graph:
+    input: x
+    modules:
+      body: [x]
+    output: body
+
+_sac_net: &sac_net
+  type: TorchArcNet
+  arc: *arc_body_256
+  hid_layers_activation: relu
+  init_fn: orthogonal_
+  clip_grad_val: null
+  loss_spec:
+    name: MSELoss
+  optim_spec:
+    name: Adam
+    lr: 0.0003
+  update_type: polyak
+  update_frequency: 1
+  polyak_coef: 0.005
+  gpu: auto
+
+_memory: &memory
+  name: Replay
+  batch_size: 256
+  max_size: 1000000
+  use_cer: false
+
+_meta: &meta
+  distributed: false
+  log_frequency: 10000
+  eval_frequency: 10000
+  rigorous_eval: 0
+  max_session: 4
+  max_trial: 1
+
+# ============================================================
+# HOPPER EXPERIMENTS
+# Current best: iter=8, 2M frames → 1M grads, ~118fps, ~4.7h, score 1510
+# Hypothesis: more data, fewer grads → same or better score, faster
+# ============================================================
+
+# Config A: iter=4, 4M frames → 1M grads (same budget), ~200fps, ~5.6h
+# Same total gradients, 2x more data, slightly longer but faster FPS
+sac_hopper_4m_i4_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: default
+      action_policy: default
+      gamma: 0.99
+      training_frequency: 1
+      training_iter: 4
+      training_start_step: 10000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: Hopper-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 4000000
+  meta: *meta
+
+# Config B: iter=4, 3M frames → 750K grads (25% fewer), ~200fps, ~4.2h
+# Fewer gradients but more data than current. Faster wall time.
+sac_hopper_3m_i4_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: default
+      action_policy: default
+      gamma: 0.99
+      training_frequency: 1
+      training_iter: 4
+      training_start_step: 10000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: Hopper-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 3000000
+  meta: *meta
+
+# ============================================================
+# WALKER2D EXPERIMENTS
+# Current best: iter=8, 2M frames → 1M grads, ~104fps, ~5.3h, score 2288
+# Same hypothesis as Hopper
+# ============================================================
+
+# Config A: iter=4, 4M frames → 1M grads (same budget), ~200fps, ~5.6h
+sac_walker2d_4m_i4_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: default
+      action_policy: default
+      gamma: 0.99
+      training_frequency: 1
+      training_iter: 4
+      training_start_step: 10000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: Walker2d-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 4000000
+  meta: *meta
+
+# Config B: iter=4, 3M frames → 750K grads (25% fewer), ~200fps, ~4.2h
+sac_walker2d_3m_i4_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: default
+      action_policy: default
+      gamma: 0.99
+      training_frequency: 1
+      training_iter: 4
+      training_start_step: 10000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: Walker2d-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 3000000
+  meta: *meta
+
+# ============================================================
+# HALFCHEETAH EXPERIMENT
+# Current: iter=4, 2M frames → 500K grads, ~200fps, ~2.8h, score 7255
+# Test: can iter=2 with 4M frames match it faster?
+# ============================================================
+
+# iter=2, 4M frames → 500K grads (same budget), ~350fps est, ~3.2h
+sac_halfcheetah_4m_i2_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: default
+      action_policy: default
+      gamma: 0.99
+      training_frequency: 1
+      training_iter: 2
+      training_start_step: 5000
+    memory: *memory
+    net: *sac_net
+  env:
+    name: HalfCheetah-v5
+    num_envs: 16
+    max_t: null
+    max_frame: 4000000
+  meta: *meta
diff --git a/slm_lab/spec/benchmark_arc/sarsa/sarsa_arc.yaml b/slm_lab/spec/benchmark_arc/sarsa/sarsa_arc.yaml
new file mode 100644
index 000000000..4c790a20e
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/sarsa/sarsa_arc.yaml
@@ -0,0 +1,127 @@
+# SARSA Classic Control - TorchArcNet specs
+# Covers: CartPole-v1 (epsilon_greedy, boltzmann, final variants)
+
+# -- Shared anchors --
+
+_sarsa_meta: &sarsa_meta
+  distributed: false
+  eval_frequency: 500
+  max_session: 4
+  max_trial: 1
+  log_frequency: 500
+
+_sarsa_algorithm: &sarsa_algorithm
+  name: SARSA
+  action_pdtype: Argmax
+  action_policy: epsilon_greedy
+  gamma: 0.99
+  training_frequency: 32
+
+_sarsa_net: &sarsa_net
+  type: TorchArcNet
+  arc:
+    modules:
+      body:
+        Sequential:
+          - LazyLinear: {out_features: 128}
+          - Tanh:
+    graph:
+      input: x
+      modules:
+        body: [x]
+      output: body
+  hid_layers_activation: tanh
+  clip_grad_val: 0.344
+  loss_spec:
+    name: MSELoss
+  optim_spec:
+    name: RMSprop
+    lr: 0.0087
+
+# -- Specs --
+
+sarsa_epsilon_greedy_cartpole_arc:
+  agent:
+    name: SARSA
+    algorithm:
+      <<: *sarsa_algorithm
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.055
+        start_step: 0
+        end_step: 10000
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *sarsa_net
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta:
+    <<: *sarsa_meta
+
+sarsa_epsilon_greedy_cartpole_final_arc:
+  agent:
+    name: SARSA
+    algorithm:
+      <<: *sarsa_algorithm
+      gamma: 0.985
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.055
+        start_step: 0
+        end_step: 12000
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *sarsa_net
+      clip_grad_val: 0.35
+      optim_spec:
+        name: RMSprop
+        lr: 0.008
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta:
+    <<: *sarsa_meta
+    log_frequency: 2000
+    max_trial: 12
+  search:
+    agent.algorithm.gamma__choice: [0.985, 0.99, 0.995]
+    agent.algorithm.explore_var_spec.end_val__choice: [0.055, 0.06]
+    agent.algorithm.explore_var_spec.end_step__choice: [10000, 12000, 15000]
+    agent.net.clip_grad_val__uniform: [0.32, 0.4]
+    agent.net.optim_spec.lr__uniform: [0.0065, 0.0095]
+
+sarsa_boltzmann_cartpole_arc:
+  agent:
+    name: SARSA
+    algorithm:
+      <<: *sarsa_algorithm
+      explore_var_spec:
+        name: linear_decay
+        start_val: 1.0
+        end_val: 0.05
+        start_step: 0
+        end_step: 15000
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      <<: *sarsa_net
+      clip_grad_val: 0.35
+      optim_spec:
+        name: RMSprop
+        lr: 0.0087
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta:
+    <<: *sarsa_meta
diff --git a/slm_lab/spec/benchmark_arc/sil/sil_arc.yaml b/slm_lab/spec/benchmark_arc/sil/sil_arc.yaml
new file mode 100644
index 000000000..223677ab1
--- /dev/null
+++ b/slm_lab/spec/benchmark_arc/sil/sil_arc.yaml
@@ -0,0 +1,142 @@
+# PPO-SIL Classic Control - TorchArcNet specs
+# Covers: CartPole-v1, Acrobot-v1
+
+# -- Shared anchors --
+
+_sil_meta: &sil_meta
+  distributed: false
+  log_frequency: 500
+  eval_frequency: 256
+  max_session: 4
+  max_trial: 1
+
+_sil_net: &sil_net
+  type: TorchArcNet
+  shared: false
+  arc:
+    modules:
+      body:
+        Sequential:
+          - LazyLinear: {out_features: 64}
+          - Tanh:
+          - LazyLinear: {out_features: 64}
+          - Tanh:
+    graph:
+      input: x
+      modules:
+        body: [x]
+      output: body
+  hid_layers_activation: tanh
+  clip_grad_val: 0.5
+  use_same_optim: false
+  loss_spec:
+    name: MSELoss
+  gpu: auto
+
+_sil_algorithm: &sil_algorithm
+  name: PPOSIL
+  action_pdtype: default
+  action_policy: default
+  explore_var_spec: null
+  gamma: 0.99
+  lam: 0.95
+  clip_eps_spec: &clip_02
+    name: no_decay
+    start_val: 0.2
+    end_val: 0.2
+    start_step: 0
+    end_step: 0
+  entropy_coef_spec: &entropy_001
+    name: no_decay
+    start_val: 0.01
+    end_val: 0.01
+    start_step: 0
+    end_step: 0
+  val_loss_coef: 0.5
+  sil_policy_loss_coef: 1.0
+  sil_val_loss_coef: 0.01
+  time_horizon: 256
+  minibatch_size: 128
+  training_batch_iter: 4
+  training_iter: 4
+  training_epoch: 10
+
+_sil_memory: &sil_memory
+  name: OnPolicyBatchReplay
+  sil_replay_name: Replay
+  batch_size: 128
+  max_size: 10000
+  use_cer: true
+
+# -- Specs --
+
+ppo_sil_cartpole_arc:
+  agent:
+    name: PPOSIL
+    algorithm:
+      <<: *sil_algorithm
+    memory:
+      <<: *sil_memory
+    net:
+      <<: *sil_net
+      actor_optim_spec:
+        name: Adam
+        lr: 0.0003
+      critic_optim_spec:
+        name: Adam
+        lr: 0.0003
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta:
+    <<: *sil_meta
+
+ppo_sil_acrobot_arc:
+  agent:
+    name: PPOSIL
+    algorithm:
+      <<: *sil_algorithm
+      gamma: 0.995
+      lam: 0.947
+      sil_policy_loss_coef: 0.58
+      sil_val_loss_coef: 0.029
+      minibatch_size: 64
+      normalize_v_targets: true
+    memory:
+      <<: *sil_memory
+    net:
+      <<: *sil_net
+      init_fn: orthogonal_
+      actor_init_std: 0.01
+      critic_init_std: 1.0
+      actor_optim_spec:
+        name: AdamW
+        lr: 0.00012
+      critic_optim_spec:
+        name: AdamW
+        lr: 0.00174
+  env:
+    name: Acrobot-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 500000
+  meta:
+    <<: *sil_meta
+    eval_frequency: 500
+    rigorous_eval: 0
+    max_trial: 20
+    search_resources:
+      cpu: 1
+      gpu: 0.125
+    search_scheduler:
+      grace_period: 50000
+      reduction_factor: 3
+  search:
+    agent.algorithm.gamma__uniform: [0.98, 0.999]
+    agent.algorithm.lam__uniform: [0.9, 0.98]
+    agent.algorithm.sil_policy_loss_coef__loguniform: [0.5, 2.0]
+    agent.algorithm.sil_val_loss_coef__loguniform: [0.005, 0.05]
+    agent.net.actor_optim_spec.lr__loguniform: [0.0001, 0.001]
+    agent.net.critic_optim_spec.lr__loguniform: [0.0005, 0.003]
diff --git a/slm_lab/spec/experimental/torcharc/ppo_atari_arc.yaml b/slm_lab/spec/experimental/torcharc/ppo_atari_arc.yaml
new file mode 100644
index 000000000..4bca3b664
--- /dev/null
+++ b/slm_lab/spec/experimental/torcharc/ppo_atari_arc.yaml
@@ -0,0 +1,92 @@
+# PPO Atari using TorchArcNet - equivalent to ppo_atari ConvNet spec.
+# Uses LazyConv2d/LazyLinear so input dims are inferred at first forward pass.
+# Uses normalize=true in net spec to normalize pixel inputs to [0,1] on GPU in forward pass.
+
+ppo_atari_arc:
+  agent:
+    name: PPO
+    algorithm:
+      name: PPO
+      action_pdtype: default
+      action_policy: default
+      explore_var_spec: null
+      gamma: 0.99
+      lam: 0.95
+      clip_eps_spec:
+        name: no_decay
+        start_val: 0.1
+        end_val: 0.1
+        start_step: 0
+        end_step: 0
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.01
+        end_val: 0.01
+        start_step: 0
+        end_step: 0
+      val_loss_coef: 0.5
+      time_horizon: 128
+      minibatch_size: 256
+      training_epoch: 4
+      clip_vloss: true
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyConv2d:
+                  out_channels: 32
+                  kernel_size: 8
+                  stride: 4
+              - ReLU:
+              - LazyConv2d:
+                  out_channels: 64
+                  kernel_size: 4
+                  stride: 2
+              - ReLU:
+              - LazyConv2d:
+                  out_channels: 64
+                  kernel_size: 3
+                  stride: 1
+              - ReLU:
+              - Flatten:
+              - LazyLinear:
+                  out_features: 512
+              - ReLU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      shared: true
+      hid_layers_activation: relu
+      init_fn: orthogonal_
+      clip_grad_val: 0.5
+      use_same_optim: true
+      loss_spec:
+        name: MSELoss
+      optim_spec:
+        name: AdamW
+        lr: 2.5e-4
+        eps: 1e-5
+      lr_scheduler_spec:
+        name: LinearToZero
+        frame: 10e6
+      actor_init_std: 0.01
+      critic_init_std: 1.0
+      normalize: true
+      gpu: auto
+  env:
+    name: ${env}
+    num_envs: 16
+    max_frame: 1e7
+    life_loss_info: true
+  meta:
+    distributed: false
+    eval_frequency: 10000
+    log_frequency: 10000
+    max_session: 4
+    max_trial: 1
diff --git a/slm_lab/spec/experimental/torcharc/ppo_cartpole_arc.yaml b/slm_lab/spec/experimental/torcharc/ppo_cartpole_arc.yaml
new file mode 100644
index 000000000..ce5c8110f
--- /dev/null
+++ b/slm_lab/spec/experimental/torcharc/ppo_cartpole_arc.yaml
@@ -0,0 +1,73 @@
+# PPO CartPole using TorchArcNet - demonstrates torcharc network specification
+# The 'arc' block defines the body/feature extractor using torcharc YAML format.
+# Output heads are added automatically by SLM-Lab based on action space.
+
+ppo_cartpole_arc:
+  agent:
+    name: PPO
+    algorithm:
+      name: PPO
+      action_pdtype: default
+      action_policy: default
+      explore_var_spec: null
+      gamma: 0.9769
+      lam: 0.9112
+      clip_eps_spec:
+        name: no_decay
+        start_val: 0.1312
+        end_val: 0.1312
+        start_step: 0
+        end_step: 200000
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.0074
+        end_val: 0.0074
+        start_step: 0
+        end_step: 200000
+      val_loss_coef: 0.6049
+      clip_val_loss: false
+      time_horizon: 256
+      minibatch_size: 128
+      training_epoch: 15
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear:
+                  out_features: 64
+              - Tanh:
+              - LazyLinear:
+                  out_features: 64
+              - Tanh:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      shared: false
+      clip_grad_val: 0.5
+      use_same_optim: false
+      loss_spec:
+        name: MSELoss
+      actor_optim_spec:
+        name: Adam
+        lr: 0.0005909
+      critic_optim_spec:
+        name: Adam
+        lr: 0.0006352
+      gpu: auto
+  env:
+    name: CartPole-v1
+    max_t: null
+    max_frame: 200000
+    num_envs: 4
+  meta:
+    distributed: false
+    log_frequency: 500
+    eval_frequency: 256
+    max_session: 4
+    max_trial: 1
diff --git a/slm_lab/spec/experimental/torcharc/ppo_mujoco_arc.yaml b/slm_lab/spec/experimental/torcharc/ppo_mujoco_arc.yaml
new file mode 100644
index 000000000..861475c03
--- /dev/null
+++ b/slm_lab/spec/experimental/torcharc/ppo_mujoco_arc.yaml
@@ -0,0 +1,78 @@
+# PPO MuJoCo using TorchArcNet - equivalent to ppo_mujoco.json with MLPNet
+# The 'arc' block defines the body/feature extractor using torcharc YAML format.
+# Output heads are added automatically by SLM-Lab based on action space.
+
+ppo_mujoco_arc:
+  agent:
+    name: PPO
+    algorithm:
+      name: PPO
+      action_pdtype: default
+      action_policy: default
+      explore_var_spec: null
+      gamma: 0.99
+      lam: 0.95
+      clip_eps_spec:
+        name: no_decay
+        start_val: 0.2
+        end_val: 0.2
+        start_step: 0
+        end_step: 0
+      entropy_coef_spec:
+        name: no_decay
+        start_val: 0.0
+        end_val: 0.0
+        start_step: 0
+        end_step: 0
+      val_loss_coef: 0.5
+      time_horizon: 2048
+      minibatch_size: 64
+      training_epoch: 10
+      normalize_v_targets: true
+    memory:
+      name: OnPolicyBatchReplay
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear:
+                  out_features: 256
+              - Tanh:
+              - LazyLinear:
+                  out_features: 256
+              - Tanh:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      shared: false
+      hid_layers_activation: tanh
+      init_fn: orthogonal_
+      clip_grad_val: 0.5
+      use_same_optim: true
+      log_std_init: 0.0
+      actor_init_std: 0.01
+      critic_init_std: 1.0
+      loss_spec:
+        name: MSELoss
+      optim_spec:
+        name: AdamW
+        lr: 3e-4
+      gpu: auto
+  env:
+    name: ${env}
+    num_envs: 16
+    max_t: null
+    max_frame: ${max_frame}
+    normalize_obs: true
+    normalize_reward: true
+  meta:
+    distributed: false
+    log_frequency: 10000
+    eval_frequency: 10000
+    rigorous_eval: 0
+    max_session: 4
+    max_trial: 1
diff --git a/slm_lab/spec/experimental/torcharc/sac_atari_arc.yaml b/slm_lab/spec/experimental/torcharc/sac_atari_arc.yaml
new file mode 100644
index 000000000..33dba493f
--- /dev/null
+++ b/slm_lab/spec/experimental/torcharc/sac_atari_arc.yaml
@@ -0,0 +1,79 @@
+# SAC Atari using TorchArcNet - ConvNet-equivalent architecture
+# Conv layers: 32@8x8s4, 64@4x4s2, 64@3x3s1 -> Flatten -> FC 512
+# SAC creates 5 nets from the same net_spec (actor, q1, target_q1, q2, target_q2).
+# The 'arc' block defines the body; tails are auto-added per out_dim.
+
+sac_atari_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: Categorical
+      action_policy: default
+      gamma: 0.99
+      training_start_step: 10000
+      training_frequency: 4
+      training_iter: 1
+    memory:
+      name: Replay
+      batch_size: 256
+      max_size: 200000
+      use_cer: false
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyConv2d:
+                  out_channels: 32
+                  kernel_size: 8
+                  stride: 4
+              - ReLU:
+              - LazyConv2d:
+                  out_channels: 64
+                  kernel_size: 4
+                  stride: 2
+              - ReLU:
+              - LazyConv2d:
+                  out_channels: 64
+                  kernel_size: 3
+                  stride: 1
+              - ReLU:
+              - Flatten:
+              - LazyLinear:
+                  out_features: 512
+              - ReLU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      shared: false
+      hid_layers_activation: relu
+      init_fn: orthogonal_
+      clip_grad_val: 0.5
+      use_same_optim: false
+      loss_spec:
+        name: SmoothL1Loss
+      optim_spec:
+        name: AdamW
+        lr: 0.0003
+        eps: 0.0001
+      update_type: polyak
+      update_frequency: 1
+      polyak_coef: 0.005
+      normalize: true
+      gpu: auto
+  env:
+    name: ${env}
+    num_envs: 16
+    max_t: null
+    max_frame: 1e7
+    life_loss_info: true
+  meta:
+    distributed: false
+    eval_frequency: 10000
+    log_frequency: 10000
+    max_session: 4
+    max_trial: 1
diff --git a/slm_lab/spec/experimental/torcharc/sac_cartpole_arc.yaml b/slm_lab/spec/experimental/torcharc/sac_cartpole_arc.yaml
new file mode 100644
index 000000000..decc0a065
--- /dev/null
+++ b/slm_lab/spec/experimental/torcharc/sac_cartpole_arc.yaml
@@ -0,0 +1,56 @@
+# SAC CartPole using TorchArcNet - demonstrates torcharc network specification
+# SAC creates 5 nets from the same net_spec (actor, q1, target_q1, q2, target_q2).
+# The 'arc' block defines the body; tails are auto-added per out_dim.
+
+sac_cartpole_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm:
+      name: SoftActorCritic
+      action_pdtype: Categorical
+      action_policy: default
+      gamma: 0.99
+      training_frequency: 1
+      training_start_step: 1000
+    memory:
+      name: Replay
+      batch_size: 256
+      max_size: 100000
+      use_cer: false
+    net:
+      type: TorchArcNet
+      arc:
+        modules:
+          body:
+            Sequential:
+              - LazyLinear:
+                  out_features: 256
+              - ReLU:
+              - LazyLinear:
+                  out_features: 256
+              - ReLU:
+        graph:
+          input: x
+          modules:
+            body: [x]
+          output: body
+      clip_grad_val: null
+      loss_spec:
+        name: MSELoss
+      optim_spec:
+        name: Adam
+        lr: 0.0003
+      update_type: polyak
+      polyak_coef: 0.005
+      gpu: auto
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta:
+    distributed: false
+    log_frequency: 500
+    eval_frequency: 500
+    max_session: 4
+    max_trial: 1
diff --git a/slm_lab/spec/experimental/torcharc/sac_classic_arc.yaml b/slm_lab/spec/experimental/torcharc/sac_classic_arc.yaml
new file mode 100644
index 000000000..c65b4e192
--- /dev/null
+++ b/slm_lab/spec/experimental/torcharc/sac_classic_arc.yaml
@@ -0,0 +1,95 @@
+# SAC Classic Control suite using TorchArcNet with YAML anchors
+# Demonstrates how YAML anchors eliminate duplication across environments.
+# Only the env-specific overrides differ between specs.
+
+# Shared building blocks via YAML anchors
+_arc_body: &arc_body
+  modules:
+    body:
+      Sequential:
+        - LazyLinear:
+            out_features: 256
+        - ReLU:
+        - LazyLinear:
+            out_features: 256
+        - ReLU:
+  graph:
+    input: x
+    modules:
+      body: [x]
+    output: body
+
+_sac_net: &sac_net
+  type: TorchArcNet
+  arc: *arc_body
+  clip_grad_val: null
+  loss_spec:
+    name: MSELoss
+  optim_spec:
+    name: Adam
+    lr: 0.0003
+  update_type: polyak
+  polyak_coef: 0.005
+  gpu: auto
+
+_sac_algorithm: &sac_algorithm
+  name: SoftActorCritic
+  action_pdtype: Categorical
+  action_policy: default
+  gamma: 0.99
+  training_frequency: 1
+  training_start_step: 1000
+
+_sac_memory: &sac_memory
+  name: Replay
+  batch_size: 256
+  max_size: 100000
+  use_cer: false
+
+_sac_meta: &sac_meta
+  distributed: false
+  log_frequency: 500
+  eval_frequency: 500
+  max_session: 4
+  max_trial: 1
+
+# --- Specs: only env differs ---
+
+sac_cartpole_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm: *sac_algorithm
+    memory: *sac_memory
+    net: *sac_net
+  env:
+    name: CartPole-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 200000
+  meta: *sac_meta
+
+sac_acrobot_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm: *sac_algorithm
+    memory: *sac_memory
+    net: *sac_net
+  env:
+    name: Acrobot-v1
+    num_envs: 4
+    max_t: null
+    max_frame: 300000
+  meta: *sac_meta
+
+sac_mountaincar_arc:
+  agent:
+    name: SoftActorCritic
+    algorithm: *sac_algorithm
+    memory: *sac_memory
+    net: *sac_net
+  env:
+    name: MountainCar-v0
+    num_envs: 4
+    max_t: null
+    max_frame: 300000
+  meta: *sac_meta
diff --git a/slm_lab/spec/spec_util.py b/slm_lab/spec/spec_util.py
index 7600643fd..43b78e431 100644
--- a/slm_lab/spec/spec_util.py
+++ b/slm_lab/spec/spec_util.py
@@ -10,14 +10,14 @@
 from slm_lab.lib import logger, util
 from slm_lab.lib.env_var import lab_mode
 
-SPEC_DIR = 'slm_lab/spec'
-'''
+SPEC_DIR = "slm_lab/spec"
+"""
 All spec values are already param, inferred automatically.
 To change from a value into param range, e.g.
 - single: "explore_anneal_epi": 50
 - continuous param: "explore_anneal_epi": {"min": 50, "max": 100, "dist": "uniform"}
 - discrete range: "explore_anneal_epi": {"values": [50, 75, 100]}
-'''
+"""
 SPEC_FORMAT = {
     "agent": {
         "name": str,
@@ -40,24 +40,32 @@
 
 
 def check_comp_spec(comp_spec, comp_spec_format):
-    '''Base method to check component spec'''
+    """Base method to check component spec"""
     for spec_k, spec_format_v in comp_spec_format.items():
         if spec_k not in comp_spec:
             continue  # Optional key not specified
         comp_spec_v = comp_spec[spec_k]
         if ps.is_list(spec_format_v):
             v_set = spec_format_v
-            assert comp_spec_v in v_set, f'Component spec value {ps.pick(comp_spec, spec_k)} needs to be one of {util.to_json(v_set)}'
+            assert comp_spec_v in v_set, (
+                f"Component spec value {ps.pick(comp_spec, spec_k)} needs to be one of {util.to_json(v_set)}"
+            )
         else:
             v_type = spec_format_v
-            assert isinstance(comp_spec_v, v_type), f'Component spec {ps.pick(comp_spec, spec_k)} needs to be of type: {v_type}'
-            if isinstance(v_type, tuple) and int in v_type and isinstance(comp_spec_v, float):
+            assert isinstance(comp_spec_v, v_type), (
+                f"Component spec {ps.pick(comp_spec, spec_k)} needs to be of type: {v_type}"
+            )
+            if (
+                isinstance(v_type, tuple)
+                and int in v_type
+                and isinstance(comp_spec_v, float)
+            ):
                 # cast if it can be int
                 comp_spec[spec_k] = int(comp_spec_v)
 
 
 def check_compatibility(spec):
-    '''Check compatibility among spec setups'''
+    """Check compatibility among spec setups"""
     # A3C Hogwild (distributed=synced) is CPU-only by design:
     # - PyTorch share_memory_() only works on CPU tensors
     # - Global nets must be shared across processes for Hogwild updates
@@ -66,58 +74,64 @@ def check_compatibility(spec):
 
 
 def check(spec):
-    '''Check a single spec for validity'''
+    """Check a single spec for validity"""
     try:
-        spec_name = spec.get('name')
-        assert set(spec.keys()) >= set(SPEC_FORMAT.keys()), f'Spec needs to follow spec.SPEC_FORMAT. Given \n {spec_name}: {util.to_json(spec)}'
-        check_comp_spec(spec['agent'], SPEC_FORMAT['agent'])
-        check_comp_spec(spec['env'], SPEC_FORMAT['env'])
-        check_comp_spec(spec['meta'], SPEC_FORMAT['meta'])
+        spec_name = spec.get("name")
+        assert set(spec.keys()) >= set(SPEC_FORMAT.keys()), (
+            f"Spec needs to follow spec.SPEC_FORMAT. Given \n {spec_name}: {util.to_json(spec)}"
+        )
+        check_comp_spec(spec["agent"], SPEC_FORMAT["agent"])
+        check_comp_spec(spec["env"], SPEC_FORMAT["env"])
+        check_comp_spec(spec["meta"], SPEC_FORMAT["meta"])
         check_compatibility(spec)
     except Exception as e:
-        logger.exception(f'spec {spec_name} fails spec check')
+        logger.exception(f"spec {spec_name} fails spec check")
         raise e
     return True
 
 
 def check_all():
-    '''Check all spec files, all specs.'''
-    spec_files = ps.filter_(os.listdir(SPEC_DIR), lambda f: f.endswith('.json') and not f.startswith('_'))
+    """Check all spec files, all specs."""
+    spec_files = ps.filter_(
+        os.listdir(SPEC_DIR),
+        lambda f: (f.endswith(".json") or f.endswith(".yaml") or f.endswith(".yml"))
+        and not f.startswith("_"),
+    )
     for spec_file in spec_files:
-        spec_dict = util.read(f'{SPEC_DIR}/{spec_file}')
+        spec_dict = util.read(f"{SPEC_DIR}/{spec_file}")
         for spec_name, spec in spec_dict.items():
             # fill-in info at runtime
-            spec['name'] = spec_name
+            spec["name"] = spec_name
             spec = extend_meta_spec(spec)
             try:
                 check(spec)
             except Exception as e:
-                logger.exception(f'spec_file {spec_file} fails spec check')
+                logger.exception(f"spec_file {spec_file} fails spec check")
                 raise e
-    logger.info(f'Checked all specs from: {ps.join(spec_files, ",")}')
+    logger.info(f"Checked all specs from: {ps.join(spec_files, ',')}")
     return True
 
 
 def extend_meta_spec(spec, experiment_ts=None):
-    '''
+    """
     Extend meta spec with information for lab functions
     @param dict:spec
     @param str:experiment_ts Use this experiment_ts if given; used for resuming training
-    '''
+    """
     extended_meta_spec = {
-        'rigorous_eval': ps.get(spec, 'meta.rigorous_eval', 0),
+        "rigorous_eval": ps.get(spec, "meta.rigorous_eval", 0),
         # reset lab indices to -1 so that they tick to 0
-        'experiment': -1,
-        'trial': -1,
-        'session': -1,
-        'cuda_offset': int(os.environ.get('CUDA_OFFSET', 0)),
-        'resume': experiment_ts is not None,
-        'experiment_ts': experiment_ts or util.get_ts(),
-        'prepath': None,
-        'git_sha': util.get_git_sha(),
-        'random_seed': None,
+        "experiment": -1,
+        "trial": -1,
+        "session": -1,
+        "cuda_offset": int(os.environ.get("CUDA_OFFSET", 0)),
+        "resume": experiment_ts is not None,
+        "experiment_ts": experiment_ts or util.get_ts(),
+        "prepath": None,
+        "git_sha": util.get_git_sha(),
+        "random_seed": None,
     }
-    spec['meta'].update(extended_meta_spec)
+    spec["meta"].update(extended_meta_spec)
     return spec
 
 
@@ -144,7 +158,7 @@ def set_variables(spec_str: str, sets: list[str] | None) -> tuple[str, str | Non
 
 
 def get(spec_file, spec_name, experiment_ts=None, sets: list[str] | None = None):
-    '''
+    """
     Get an experiment spec from spec_file, spec_name.
     @param str:spec_file
     @param str:spec_name
@@ -154,82 +168,91 @@ def get(spec_file, spec_name, experiment_ts=None, sets: list[str] | None = None)
 
     spec = spec_util.get('demo.json', 'ppo_cartpole')
     spec = spec_util.get('ppo_mujoco.json', 'ppo_mujoco', sets=['env=Hopper-v5', 'max_frame=3e6'])
-    '''
-    spec_file = spec_file.replace(SPEC_DIR, '')  # guard
-    spec_file = f'{SPEC_DIR}/{spec_file}'  # allow direct filename
+    """
+    spec_file = spec_file.replace(SPEC_DIR, "")  # guard
+    spec_file = f"{SPEC_DIR}/{spec_file}"  # allow direct filename
 
-    # Read raw JSON, substitute variables, then parse
+    # Read raw file, substitute variables, then parse
     with open(spec_file) as f:
         spec_str = f.read()
     spec_str, env_short = set_variables(spec_str, sets)
-    spec_dict = json.loads(spec_str)
 
-    assert spec_name in spec_dict, f'spec_name {spec_name} is not in spec_file {spec_file}. Choose from:\n {ps.join(spec_dict.keys(), ",")}'
+    ext = os.path.splitext(spec_file)[-1]
+    if ext in (".yaml", ".yml"):
+        import yaml
+
+        spec_dict = yaml.safe_load(spec_str)
+    else:
+        spec_dict = json.loads(spec_str)
+
+    assert spec_name in spec_dict, (
+        f"spec_name {spec_name} is not in spec_file {spec_file}. Choose from:\n {ps.join(spec_dict.keys(), ',')}"
+    )
     spec = spec_dict[spec_name]
     # fill-in info at runtime
-    spec['name'] = spec_name
+    spec["name"] = spec_name
     if env_short:
-        spec['name'] = f"{spec['name']}_{env_short}"
+        spec["name"] = f"{spec['name']}_{env_short}"
     spec = extend_meta_spec(spec, experiment_ts)
     check(spec)
     return spec
 
 
 def _override_dev_spec(spec):
-    spec['meta']['max_session'] = 1
-    spec['meta']['max_trial'] = 2
+    spec["meta"]["max_session"] = 1
+    spec["meta"]["max_trial"] = 2
     return spec
 
 
 def _override_enjoy_spec(spec):
-    spec['meta']['max_session'] = 1
+    spec["meta"]["max_session"] = 1
     return spec
 
 
 def _override_test_spec(spec):
-    agent_spec = spec['agent']
+    agent_spec = spec["agent"]
     # onpolicy freq is episodic
-    freq = 1 if agent_spec['memory']['name'] == 'OnPolicyReplay' else 8
-    agent_spec['algorithm']['training_frequency'] = freq
-    agent_spec['algorithm']['time_horizon'] = freq
-    agent_spec['algorithm']['training_start_step'] = 1
-    agent_spec['algorithm']['training_iter'] = 1
-    agent_spec['algorithm']['training_batch_iter'] = 1
-    
-    env_spec = spec['env']
-    env_spec['max_frame'] = 40
-    if env_spec.get('num_envs', 1) > 1:
-        env_spec['num_envs'] = 2
-    env_spec['max_t'] = 12
-    
-    spec['meta']['log_frequency'] = 10
-    spec['meta']['eval_frequency'] = 10
-    spec['meta']['max_session'] = 1
-    spec['meta']['max_trial'] = 2
+    freq = 1 if agent_spec["memory"]["name"] == "OnPolicyReplay" else 8
+    agent_spec["algorithm"]["training_frequency"] = freq
+    agent_spec["algorithm"]["time_horizon"] = freq
+    agent_spec["algorithm"]["training_start_step"] = 1
+    agent_spec["algorithm"]["training_iter"] = 1
+    agent_spec["algorithm"]["training_batch_iter"] = 1
+
+    env_spec = spec["env"]
+    env_spec["max_frame"] = 40
+    if env_spec.get("num_envs", 1) > 1:
+        env_spec["num_envs"] = 2
+    env_spec["max_t"] = 12
+
+    spec["meta"]["log_frequency"] = 10
+    spec["meta"]["eval_frequency"] = 10
+    spec["meta"]["max_session"] = 1
+    spec["meta"]["max_trial"] = 2
     return spec
 
 
 def override_spec(spec, mode):
-    '''Override spec based on the (lab_)mode, do nothing otherwise.'''
+    """Override spec based on the (lab_)mode, do nothing otherwise."""
     overrider = {
-        'dev': _override_dev_spec,
-        'enjoy': _override_enjoy_spec,
-        'test': _override_test_spec,
+        "dev": _override_dev_spec,
+        "enjoy": _override_enjoy_spec,
+        "test": _override_test_spec,
     }.get(mode)
     if overrider is not None:
         overrider(spec)
     return spec
 
 
-def save(spec, unit='experiment'):
-    '''Save spec to proper path. Called at Experiment or Trial init.'''
+def save(spec, unit="experiment"):
+    """Save spec to proper path. Called at Experiment or Trial init."""
     prepath = util.get_prepath(spec, unit)
-    util.write(spec, f'{prepath}_spec.json')
+    util.write(spec, f"{prepath}_spec.json")
 
 
 def get_best_session(spec) -> int:
-    '''Find best performing session based on total_reward_ma.'''
-    info_prepath = spec['meta'].get('info_prepath')
+    """Find best performing session based on total_reward_ma."""
+    info_prepath = spec["meta"].get("info_prepath")
     if not info_prepath:
         return 0
     pattern = f"{ROOT_DIR}/{info_prepath}_s*_session_metrics_scalar_train.json"
@@ -237,13 +260,13 @@ def get_best_session(spec) -> int:
     if not files:
         return 0
 
-    best_session, best_reward = 0, float('-inf')
+    best_session, best_reward = 0, float("-inf")
     for f in files:
-        match = re.search(r'_t\d+_s(\d+)_', f)
+        match = re.search(r"_t\d+_s(\d+)_", f)
         if not match:
             continue
         try:
-            reward = util.read(f).get('total_reward_ma', float('-inf'))
+            reward = util.read(f).get("total_reward_ma", float("-inf"))
             if reward > best_reward:
                 best_reward, best_session = reward, int(match.group(1))
         except Exception:
@@ -253,55 +276,55 @@ def get_best_session(spec) -> int:
 
 
 def tick(spec, unit):
-    '''
+    """
     Method to tick lab unit (experiment, trial, session) in meta spec to advance their indices
     Reset lower lab indices to -1 so that they tick to 0
     spec_util.tick(spec, 'session')
     session = Session(spec)
-    '''
-    meta_spec = spec['meta']
-    is_enjoy = lab_mode() == 'enjoy'
+    """
+    meta_spec = spec["meta"]
+    is_enjoy = lab_mode() == "enjoy"
 
     # Advance indices based on unit
-    if unit == 'experiment':
+    if unit == "experiment":
         if not is_enjoy:
-            meta_spec['experiment_ts'] = util.get_ts()
-        meta_spec['experiment'] += 1
-        meta_spec['trial'] = -1
-        meta_spec['session'] = -1
-    elif unit == 'trial':
-        if meta_spec['experiment'] == -1:
-            meta_spec['experiment'] += 1
-        meta_spec['trial'] += 1
-        meta_spec['session'] = -1
-    elif unit == 'session':
-        if meta_spec['experiment'] == -1:
-            meta_spec['experiment'] += 1
-        if meta_spec['trial'] == -1:
-            meta_spec['trial'] += 1
+            meta_spec["experiment_ts"] = util.get_ts()
+        meta_spec["experiment"] += 1
+        meta_spec["trial"] = -1
+        meta_spec["session"] = -1
+    elif unit == "trial":
+        if meta_spec["experiment"] == -1:
+            meta_spec["experiment"] += 1
+        meta_spec["trial"] += 1
+        meta_spec["session"] = -1
+    elif unit == "session":
+        if meta_spec["experiment"] == -1:
+            meta_spec["experiment"] += 1
+        if meta_spec["trial"] == -1:
+            meta_spec["trial"] += 1
         # In enjoy mode, use best performing session; otherwise increment normally
         if is_enjoy:
-            meta_spec['session'] = get_best_session(spec)
+            meta_spec["session"] = get_best_session(spec)
         else:
-            meta_spec['session'] += 1
+            meta_spec["session"] += 1
     else:
-        raise ValueError(f'Unrecognized lab unit to tick: {unit}')
+        raise ValueError(f"Unrecognized lab unit to tick: {unit}")
 
     if is_enjoy:
-        prepath = util.get_prepath(spec, unit='session')
-        meta_spec['prepath'] = prepath
-        for folder in ('graph', 'info', 'log', 'model'):
-            meta_spec[f'{folder}_prepath'] = util.insert_folder(prepath, folder)
+        prepath = util.get_prepath(spec, unit="session")
+        meta_spec["prepath"] = prepath
+        for folder in ("graph", "info", "log", "model"):
+            meta_spec[f"{folder}_prepath"] = util.insert_folder(prepath, folder)
         return spec
 
     # Set prepath/predir and create directories for training
-    meta_spec['predir'] = util.get_predir(spec)
-    meta_spec['prepath'] = prepath = util.get_prepath(spec, unit)
-    for folder in ('graph', 'info', 'log', 'model'):
+    meta_spec["predir"] = util.get_predir(spec)
+    meta_spec["prepath"] = prepath = util.get_prepath(spec, unit)
+    for folder in ("graph", "info", "log", "model"):
         folder_prepath = util.insert_folder(prepath, folder)
-        folder_predir = os.path.dirname(f'{ROOT_DIR}/{folder_prepath}')
+        folder_predir = os.path.dirname(f"{ROOT_DIR}/{folder_prepath}")
         os.makedirs(folder_predir, exist_ok=True)
         assert os.path.exists(folder_predir)
-        meta_spec[f'{folder}_prepath'] = folder_prepath
+        meta_spec[f"{folder}_prepath"] = folder_prepath
 
     return spec
diff --git a/test/agent/net/test_torcharc_net.py b/test/agent/net/test_torcharc_net.py
new file mode 100644
index 000000000..b099d8bf3
--- /dev/null
+++ b/test/agent/net/test_torcharc_net.py
@@ -0,0 +1,299 @@
+import pytest
+import torch
+
+try:
+    import torcharc
+
+    HAS_TORCHARC = True
+except ImportError:
+    HAS_TORCHARC = False
+
+
+@pytest.mark.skipif(not HAS_TORCHARC, reason="torcharc not installed")
+class TestTorchArcNet:
+    def test_build_mlp(self):
+        from slm_lab.agent.net.torcharc_net import TorchArcNet
+
+        net_spec = {
+            "type": "TorchArcNet",
+            "arc": {
+                "modules": {
+                    "body": {
+                        "Sequential": [
+                            {"LazyLinear": {"out_features": 64}},
+                            {"ReLU": {}},
+                            {"LazyLinear": {"out_features": 64}},
+                            {"ReLU": {}},
+                        ]
+                    }
+                },
+                "graph": {
+                    "input": "x",
+                    "modules": {"body": ["x"]},
+                    "output": "body",
+                },
+            },
+            "loss_spec": {"name": "MSELoss"},
+            "optim_spec": {"name": "Adam", "lr": 0.001},
+            "gpu": False,
+        }
+
+        # Test with discrete action space (single int out_dim)
+        net = TorchArcNet(net_spec, in_dim=4, out_dim=2)
+        x = torch.randn(8, 4)
+        out = net(x)
+        assert out.shape == (8, 2)
+
+    def test_build_multi_output(self):
+        from slm_lab.agent.net.torcharc_net import TorchArcNet
+
+        net_spec = {
+            "type": "TorchArcNet",
+            "arc": {
+                "modules": {
+                    "body": {
+                        "Sequential": [
+                            {"LazyLinear": {"out_features": 64}},
+                            {"Tanh": {}},
+                        ]
+                    }
+                },
+                "graph": {
+                    "input": "x",
+                    "modules": {"body": ["x"]},
+                    "output": "body",
+                },
+            },
+            "loss_spec": {"name": "MSELoss"},
+            "optim_spec": {"name": "Adam", "lr": 0.001},
+            "gpu": False,
+        }
+
+        # Test with actor-critic output (list out_dim)
+        net = TorchArcNet(net_spec, in_dim=4, out_dim=[2, 1])
+        x = torch.randn(8, 4)
+        out = net(x)
+        assert isinstance(out, list)
+        assert len(out) == 2
+        assert out[0].shape == (8, 2)
+        assert out[1].shape == (8, 1)
+
+    def test_with_optim(self):
+        from slm_lab.agent.net.torcharc_net import TorchArcNet
+        from slm_lab.agent.net import net_util
+
+        net_spec = {
+            "type": "TorchArcNet",
+            "arc": {
+                "modules": {
+                    "body": {
+                        "Sequential": [
+                            {"LazyLinear": {"out_features": 32}},
+                            {"ReLU": {}},
+                        ]
+                    }
+                },
+                "graph": {
+                    "input": "x",
+                    "modules": {"body": ["x"]},
+                    "output": "body",
+                },
+            },
+            "loss_spec": {"name": "MSELoss"},
+            "optim_spec": {"name": "Adam", "lr": 0.001},
+            "gpu": False,
+        }
+        net = TorchArcNet(net_spec, in_dim=4, out_dim=2)
+        optim = net_util.get_optim(net, net.optim_spec)
+        assert optim is not None
+
+        # Test backward pass
+        x = torch.randn(8, 4)
+        out = net(x)
+        loss = out.sum()
+        loss.backward()
+        optim.step()
+
+    def test_log_std(self):
+        from slm_lab.agent.net.torcharc_net import TorchArcNet
+
+        net_spec = {
+            "type": "TorchArcNet",
+            "arc": {
+                "modules": {
+                    "body": {
+                        "Sequential": [
+                            {"LazyLinear": {"out_features": 64}},
+                            {"Tanh": {}},
+                        ]
+                    }
+                },
+                "graph": {
+                    "input": "x",
+                    "modules": {"body": ["x"]},
+                    "output": "body",
+                },
+            },
+            "loss_spec": {"name": "MSELoss"},
+            "optim_spec": {"name": "Adam", "lr": 0.001},
+            "gpu": False,
+            "log_std_init": 0.0,
+        }
+
+        # Test with state-independent log_std (continuous actions)
+        net = TorchArcNet(net_spec, in_dim=4, out_dim=[3, 3])
+        x = torch.randn(8, 4)
+        out = net(x)
+        assert isinstance(out, list)
+        assert len(out) == 2
+        assert out[0].shape == (8, 3)  # mean
+        assert out[1].shape == (8, 3)  # log_std (expanded)
+
+    def test_conv_with_init_fn(self):
+        from slm_lab.agent.net.torcharc_net import TorchArcNet
+
+        conv_arc = {
+            "modules": {
+                "body": {
+                    "Sequential": [
+                        {
+                            "LazyConv2d": {
+                                "out_channels": 32,
+                                "kernel_size": 8,
+                                "stride": 4,
+                            }
+                        },
+                        {"ReLU": {}},
+                        {
+                            "LazyConv2d": {
+                                "out_channels": 64,
+                                "kernel_size": 4,
+                                "stride": 2,
+                            }
+                        },
+                        {"ReLU": {}},
+                        {
+                            "LazyConv2d": {
+                                "out_channels": 64,
+                                "kernel_size": 3,
+                                "stride": 1,
+                            }
+                        },
+                        {"ReLU": {}},
+                        {"Flatten": {}},
+                        {"LazyLinear": {"out_features": 512}},
+                        {"ReLU": {}},
+                    ]
+                }
+            },
+            "graph": {
+                "input": "x",
+                "modules": {"body": ["x"]},
+                "output": "body",
+            },
+        }
+        base_spec = {
+            "type": "TorchArcNet",
+            "arc": conv_arc,
+            "loss_spec": {"name": "MSELoss"},
+            "optim_spec": {"name": "Adam", "lr": 0.001},
+            "gpu": False,
+            "hid_layers_activation": "relu",
+        }
+
+        # Build without init_fn (default init)
+        net_default = TorchArcNet(base_spec, in_dim=(4, 84, 84), out_dim=[6, 1])
+        # Grab a copy of a conv weight under default init
+        default_weight = None
+        for m in net_default.modules():
+            if isinstance(m, torch.nn.Conv2d):
+                default_weight = m.weight.data.clone()
+                break
+
+        # Build with orthogonal init
+        ortho_spec = {**base_spec, "init_fn": "orthogonal_"}
+        net_ortho = TorchArcNet(ortho_spec, in_dim=(4, 84, 84), out_dim=[6, 1])
+        ortho_weight = None
+        for m in net_ortho.modules():
+            if isinstance(m, torch.nn.Conv2d):
+                ortho_weight = m.weight.data.clone()
+                break
+
+        assert default_weight is not None and ortho_weight is not None
+        # Orthogonal init should produce different weights than default
+        assert not torch.equal(default_weight, ortho_weight)
+
+        # Verify forward pass shape
+        x = torch.randn(4, 4, 84, 84)
+        out = net_ortho(x)
+        assert isinstance(out, list)
+        assert out[0].shape == (4, 6)
+        assert out[1].shape == (4, 1)
+
+    def test_shared_param(self):
+        from slm_lab.agent.net.torcharc_net import TorchArcNet
+
+        net_spec = {
+            "type": "TorchArcNet",
+            "arc": {
+                "modules": {
+                    "body": {
+                        "Sequential": [
+                            {"LazyLinear": {"out_features": 64}},
+                            {"ReLU": {}},
+                        ]
+                    }
+                },
+                "graph": {
+                    "input": "x",
+                    "modules": {"body": ["x"]},
+                    "output": "body",
+                },
+            },
+            "loss_spec": {"name": "MSELoss"},
+            "optim_spec": {"name": "Adam", "lr": 0.001},
+            "gpu": False,
+        }
+
+        # Default: shared should be False
+        net_default = TorchArcNet(net_spec, in_dim=4, out_dim=2)
+        assert net_default.shared is False
+
+        # With shared=True
+        shared_spec = {**net_spec, "shared": True}
+        net_shared = TorchArcNet(shared_spec, in_dim=4, out_dim=2)
+        assert net_shared.shared is True
+
+    def test_hid_layers_activation(self):
+        from slm_lab.agent.net.torcharc_net import TorchArcNet
+
+        net_spec = {
+            "type": "TorchArcNet",
+            "arc": {
+                "modules": {
+                    "body": {
+                        "Sequential": [
+                            {"LazyLinear": {"out_features": 64}},
+                            {"ReLU": {}},
+                        ]
+                    }
+                },
+                "graph": {
+                    "input": "x",
+                    "modules": {"body": ["x"]},
+                    "output": "body",
+                },
+            },
+            "loss_spec": {"name": "MSELoss"},
+            "optim_spec": {"name": "Adam", "lr": 0.001},
+            "gpu": False,
+        }
+
+        # Default: hid_layers_activation should be "relu"
+        net_default = TorchArcNet(net_spec, in_dim=4, out_dim=2)
+        assert net_default.hid_layers_activation == "relu"
+
+        # With tanh
+        tanh_spec = {**net_spec, "hid_layers_activation": "tanh"}
+        net_tanh = TorchArcNet(tanh_spec, in_dim=4, out_dim=2)
+        assert net_tanh.hid_layers_activation == "tanh"
diff --git a/test/cli/test_remote.py b/test/cli/test_remote.py
index 7a7ecb25c..c41f041c2 100644
--- a/test/cli/test_remote.py
+++ b/test/cli/test_remote.py
@@ -124,11 +124,8 @@ def test_dstack_apply_command_structure(self, mock_run):
 
         call_args = mock_run.call_args
         cmd = call_args[0][0]
-        assert cmd[0] == "uv"
-        assert cmd[1] == "run"
-        assert cmd[2] == "--no-default-groups"  # minimal install mode
-        assert cmd[3] == "dstack"
-        assert cmd[4] == "apply"
+        assert cmd[0] == "dstack"
+        assert cmd[1] == "apply"
         assert "-y" in cmd
         assert "--detach" in cmd
 
diff --git a/test/spec/test_yaml_spec.py b/test/spec/test_yaml_spec.py
new file mode 100644
index 000000000..d16ca758f
--- /dev/null
+++ b/test/spec/test_yaml_spec.py
@@ -0,0 +1,41 @@
+import pytest
+from slm_lab.spec import spec_util
+
+
+def test_yaml_spec_loading():
+    """Test that a YAML spec file loads correctly via spec_util.get()"""
+    spec = spec_util.get("benchmark/ppo/ppo_cartpole.yaml", "ppo_cartpole")
+    assert spec is not None
+    assert spec["name"] == "ppo_cartpole"
+    assert spec["agent"]["name"] == "PPO"
+
+
+def test_yaml_spec_check():
+    """Test that spec check works on YAML-loaded specs"""
+    spec = spec_util.get("benchmark/ppo/ppo_cartpole.yaml", "ppo_cartpole")
+    assert spec_util.check(spec)
+
+
+def test_yaml_matches_json():
+    """Test that YAML and JSON specs produce equivalent configurations"""
+    json_spec = spec_util.get("benchmark/ppo/ppo_cartpole.json", "ppo_cartpole")
+    yaml_spec = spec_util.get("benchmark/ppo/ppo_cartpole.yaml", "ppo_cartpole")
+    # Compare agent, env sections (meta has runtime-generated fields)
+    assert json_spec["agent"] == yaml_spec["agent"]
+    assert json_spec["env"] == yaml_spec["env"]
+    for key in (
+        "distributed",
+        "log_frequency",
+        "eval_frequency",
+        "max_session",
+        "max_trial",
+    ):
+        assert json_spec["meta"][key] == yaml_spec["meta"][key]
+
+
+def test_json_backward_compat():
+    """Ensure JSON specs still work after YAML support added"""
+    spec = spec_util.get("benchmark/ppo/ppo_cartpole.json", "ppo_cartpole")
+    assert spec is not None
+    assert spec["name"] == "ppo_cartpole"
+    assert spec["agent"]["name"] == "PPO"
diff --git a/uv.lock b/uv.lock
index 1f708b761..4197f7442 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,15 +2,16 @@ version = 1
 revision = 3
 requires-python = ">=3.12.0"
 resolution-markers = [
-    "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "python_version < '0'",
-    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version < '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
     "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
-    "python_full_version < '3.13' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
     "python_full_version >= '3.13' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version < '3.13' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+]
+supported-markers = [
+    "sys_platform == 'darwin'",
+    "platform_machine == 'x86_64' and sys_platform == 'linux'",
 ]
 
 [[package]]
@@ -36,50 +37,26 @@ name = "aiohttp"
 version = "3.12.15"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "aiohappyeyeballs" },
-    { name = "aiosignal" },
-    { name = "attrs" },
-    { name = "frozenlist" },
-    { name = "multidict" },
-    { name = "propcache" },
-    { name = "yarl" },
+    { name = "aiohappyeyeballs", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
+    { name = "aiosignal", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
+    { name = "attrs", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
+    { name = "frozenlist", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
+    { name = "multidict", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
+    { name = "propcache", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
+    { name = "yarl", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
 ]
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 version = "0.8.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "aiohttp" },
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-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
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+    { name = "typing-extensions", marker = "(python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux') or (python_full_version < '3.13' and sys_platform == 'darwin')" },
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