From bd02986c80ea34ddaa5eab0a2659d8796556b75a Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 11:52:25 +0100
Subject: [PATCH 01/13] Add CPU smoke test for diffusion training entrypoint

---
 dllm/__init__.py                  |  20 +++
 dllm/arc_dataset.py               | 128 +++++++++++++++
 dllm/diffusion_transformer.py     | 183 +++++++++++++++++++++
 tests/test_train_diffusion_arc.py |  71 ++++++++
 train_diffusion_arc.py            | 259 ++++++++++++++++++++++++++++++
 5 files changed, 661 insertions(+)
 create mode 100644 dllm/__init__.py
 create mode 100644 dllm/arc_dataset.py
 create mode 100644 dllm/diffusion_transformer.py
 create mode 100644 tests/test_train_diffusion_arc.py
 create mode 100644 train_diffusion_arc.py

diff --git a/dllm/__init__.py b/dllm/__init__.py
new file mode 100644
index 0000000..f275b69
--- /dev/null
+++ b/dllm/__init__.py
@@ -0,0 +1,20 @@
+"""Core modules for diffusion transformer ARC training."""
+
+from .arc_dataset import ARCTaskDataset, arc_collate
+from .diffusion_transformer import (
+    DiffusionTransformerConfig,
+    DiffusionTransformer,
+    cosine_beta_schedule,
+    build_diffusion_schedule,
+    timestep_embedding,
+)
+
+__all__ = [
+    "ARCTaskDataset",
+    "arc_collate",
+    "DiffusionTransformerConfig",
+    "DiffusionTransformer",
+    "cosine_beta_schedule",
+    "build_diffusion_schedule",
+    "timestep_embedding",
+]
diff --git a/dllm/arc_dataset.py b/dllm/arc_dataset.py
new file mode 100644
index 0000000..bd2e848
--- /dev/null
+++ b/dllm/arc_dataset.py
@@ -0,0 +1,128 @@
+"""Utilities for loading ARC-AGI style datasets."""
+
+from __future__ import annotations
+
+import json
+import random
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, List, Sequence, Tuple
+
+import torch
+from torch.utils.data import Dataset
+
+ColorGrid = List[List[int]]
+
+
+@dataclass
+class ARCExample:
+    """Single ARC training example."""
+
+    input_grid: ColorGrid
+    output_grid: ColorGrid
+
+
+def _pad_grid(
+    grid: ColorGrid,
+    max_size: int,
+    pad_value: int,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Pad a grid to ``max_size`` and return (values, mask)."""
+
+    height, width = len(grid), len(grid[0])
+    tensor = torch.full((max_size, max_size), pad_value, dtype=torch.long)
+    mask = torch.zeros((max_size, max_size), dtype=torch.float32)
+    tensor[:height, :width] = torch.tensor(grid, dtype=torch.long)
+    mask[:height, :width] = 1.0
+    return tensor.view(-1), mask.view(-1)
+
+
+class ARCTaskDataset(Dataset):
+    """Dataset that reads ARC style json files."""
+
+    def __init__(
+        self,
+        root: str | Path,
+        split: str = "training",
+        max_grid_size: int = 30,
+        pad_token_id: int = 10,
+        augment: bool = False,
+    ) -> None:
+        super().__init__()
+        self.root = Path(root)
+        self.split = split
+        self.max_grid_size = max_grid_size
+        self.pad_token_id = pad_token_id
+        self.augment = augment
+        self.examples: List[ARCExample] = []
+        split_dir = self.root / split
+        if not split_dir.exists():
+            raise FileNotFoundError(f"Split directory {split_dir} was not found.")
+        for path in sorted(split_dir.glob("*.json")):
+            with path.open("r") as fp:
+                data = json.load(fp)
+            for pair in data.get("train", []):
+                self.examples.append(
+                    ARCExample(input_grid=pair["input"], output_grid=pair["output"])
+                )
+
+    def __len__(self) -> int:  # pragma: no cover - trivial
+        return len(self.examples)
+
+    def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
+        ex = self.examples[idx]
+        input_grid, input_mask = _pad_grid(
+            ex.input_grid, self.max_grid_size, self.pad_token_id
+        )
+        target_grid, target_mask = _pad_grid(
+            ex.output_grid, self.max_grid_size, self.pad_token_id
+        )
+        sample = {
+            "condition": input_grid,
+            "condition_mask": input_mask,
+            "target": target_grid,
+            "target_mask": target_mask,
+        }
+        if self.augment and random.random() < 0.5:
+            sample = self._augment(sample)
+        return sample
+
+    def _augment(self, sample: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+        """Apply simple random flips to encourage invariances."""
+
+        side = self.max_grid_size
+        cond = sample["condition"].view(side, side)
+        targ = sample["target"].view(side, side)
+        mask_c = sample["condition_mask"].view(side, side)
+        mask_t = sample["target_mask"].view(side, side)
+
+        if random.random() < 0.5:
+            cond = torch.flip(cond, dims=[0])
+            targ = torch.flip(targ, dims=[0])
+            mask_c = torch.flip(mask_c, dims=[0])
+            mask_t = torch.flip(mask_t, dims=[0])
+        if random.random() < 0.5:
+            cond = torch.flip(cond, dims=[1])
+            targ = torch.flip(targ, dims=[1])
+            mask_c = torch.flip(mask_c, dims=[1])
+            mask_t = torch.flip(mask_t, dims=[1])
+
+        return {
+            "condition": cond.view(-1),
+            "condition_mask": mask_c.view(-1),
+            "target": targ.view(-1),
+            "target_mask": mask_t.view(-1),
+        }
+
+
+def arc_collate(batch: Sequence[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
+    condition = torch.stack([sample["condition"] for sample in batch])
+    condition_mask = torch.stack([sample["condition_mask"] for sample in batch])
+    target = torch.stack([sample["target"] for sample in batch])
+    target_mask = torch.stack([sample["target_mask"] for sample in batch])
+    return {
+        "condition": condition,
+        "condition_mask": condition_mask,
+        "target": target,
+        "target_mask": target_mask,
+    }
diff --git a/dllm/diffusion_transformer.py b/dllm/diffusion_transformer.py
new file mode 100644
index 0000000..d88db38
--- /dev/null
+++ b/dllm/diffusion_transformer.py
@@ -0,0 +1,183 @@
+"""Diffusion Transformer model tailored for ARC tasks."""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import Dict, Optional
+
+import torch
+import torch.nn as nn
+
+
+def cosine_beta_schedule(timesteps: int, s: float = 0.008) -> torch.Tensor:
+    """Cosine schedule from https://arxiv.org/abs/2102.09672."""
+
+    steps = timesteps + 1
+    x = torch.linspace(0, timesteps, steps)
+    alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * math.pi / 2) ** 2
+    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
+    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
+    return betas.clamp(1e-5, 0.999)
+
+
+@dataclass
+class DiffusionTransformerConfig:
+    vocab_size: int = 11
+    pad_token_id: int = 10
+    max_grid_size: int = 30
+    d_model: int = 288
+    num_heads: int = 8
+    num_layers: int = 7
+    dim_feedforward: int = 1152
+    dropout: float = 0.1
+    max_timesteps: int = 1000
+    time_embed_dim: int = 512
+
+    @property
+    def max_tokens(self) -> int:
+        return self.max_grid_size * self.max_grid_size
+
+    @property
+    def seq_len(self) -> int:
+        return self.max_tokens * 2
+
+
+class DiffusionTransformer(nn.Module):
+    def __init__(self, config: DiffusionTransformerConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.token_embed = nn.Embedding(config.vocab_size, config.d_model)
+        self.position_embed = nn.Embedding(config.seq_len, config.d_model)
+        self.time_embed = nn.Sequential(
+            nn.Linear(config.time_embed_dim, config.d_model * 2),
+            nn.SiLU(),
+            nn.Linear(config.d_model * 2, config.d_model),
+        )
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=config.d_model,
+            nhead=config.num_heads,
+            dim_feedforward=config.dim_feedforward,
+            dropout=config.dropout,
+            activation="gelu",
+            batch_first=True,
+            norm_first=True,
+        )
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=config.num_layers)
+        self.output_proj = nn.Linear(config.d_model, config.d_model)
+        self.layer_norm = nn.LayerNorm(config.d_model)
+        self._init_parameters()
+
+    def _init_parameters(self) -> None:
+        nn.init.normal_(self.token_embed.weight, mean=0.0, std=0.02)
+        nn.init.normal_(self.position_embed.weight, mean=0.0, std=0.02)
+        nn.init.normal_(self.output_proj.weight, mean=0.0, std=0.02)
+        if self.output_proj.bias is not None:
+            nn.init.zeros_(self.output_proj.bias)
+        with torch.no_grad():
+            self.token_embed.weight[self.config.pad_token_id].zero_()
+
+    @property
+    def pad_token_id(self) -> int:  # pragma: no cover - simple proxy
+        return self.config.pad_token_id
+
+    def forward(
+        self,
+        noisy_target: torch.Tensor,
+        condition_tokens: torch.Tensor,
+        condition_mask: torch.Tensor,
+        target_mask: torch.Tensor,
+        timesteps: torch.Tensor,
+    ) -> torch.Tensor:
+        """Predict noise for the target tokens."""
+
+        cfg = self.config
+        _, target_tokens, _ = noisy_target.shape
+        device = noisy_target.device
+        total_tokens = cfg.max_tokens
+
+        cond_pos = torch.arange(total_tokens, device=device)
+        tgt_pos = torch.arange(total_tokens, device=device) + total_tokens
+
+        cond_emb = self.token_embed(condition_tokens) + self.position_embed(cond_pos).unsqueeze(0)
+        noisy_emb = noisy_target + self.position_embed(tgt_pos).unsqueeze(0)
+
+        time_emb = timestep_embedding(timesteps, cfg.time_embed_dim).to(device)
+        time_emb = self.time_embed(time_emb)
+        cond_emb = cond_emb + time_emb.unsqueeze(1)
+        noisy_emb = noisy_emb + time_emb.unsqueeze(1)
+
+        sequence = torch.cat([cond_emb, noisy_emb], dim=1)
+        key_padding_mask = torch.cat([
+            (condition_mask == 0),
+            (target_mask == 0),
+        ], dim=1)
+        encoded = self.transformer(sequence, src_key_padding_mask=key_padding_mask)
+        encoded = self.layer_norm(encoded)
+        pred = self.output_proj(encoded[:, -target_tokens:, :])
+        return pred
+
+    def sample(
+        self,
+        condition_tokens: torch.Tensor,
+        condition_mask: torch.Tensor,
+        diffusion_schedule: Dict[str, torch.Tensor],
+        steps: Optional[int] = None,
+        guidance_scale: float = 1.0,
+    ) -> torch.Tensor:
+        """Iteratively sample a target grid given a condition."""
+
+        cfg = self.config
+        device = condition_tokens.device
+        total_tokens = cfg.max_tokens
+        timesteps = steps or cfg.max_timesteps
+        sqrt_recip_alphas = diffusion_schedule["sqrt_recip_alphas"]
+        betas = diffusion_schedule["betas"]
+        posterior_variance = diffusion_schedule["posterior_variance"]
+        target_shape = (condition_tokens.size(0), total_tokens, cfg.d_model)
+        x = torch.randn(target_shape, device=device)
+        target_mask = torch.ones_like(condition_mask)
+
+        for i in reversed(range(timesteps)):
+            t = torch.full((condition_tokens.size(0),), i, device=device, dtype=torch.long)
+            model_out = self.forward(x, condition_tokens, condition_mask, target_mask, t)
+            if guidance_scale != 1.0:
+                model_out = model_out * guidance_scale
+            sqrt_recip_alpha = sqrt_recip_alphas[i]
+            beta = betas[i]
+            model_mean = sqrt_recip_alpha * x - beta * model_out
+            if i > 0:
+                noise = torch.randn_like(x)
+                x = model_mean + torch.sqrt(posterior_variance[i]) * noise
+            else:
+                x = model_mean
+        return x
+
+
+def timestep_embedding(timesteps: torch.Tensor, dim: int, max_period: int = 10000) -> torch.Tensor:
+    half = dim // 2
+    freqs = torch.exp(-math.log(max_period) * torch.arange(half, dtype=torch.float32) / half)
+    args = timesteps.float().unsqueeze(1) * freqs.unsqueeze(0)
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    return embedding
+
+
+def build_diffusion_schedule(timesteps: int, device: torch.device) -> Dict[str, torch.Tensor]:
+    betas = cosine_beta_schedule(timesteps).to(device)
+    alphas = 1.0 - betas
+    alphas_cumprod = torch.cumprod(alphas, dim=0)
+    alphas_cumprod_prev = torch.cat([torch.ones(1, device=device), alphas_cumprod[:-1]], dim=0)
+    sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
+    sqrt_one_minus = torch.sqrt(1.0 - alphas_cumprod)
+    sqrt_recip_alphas = torch.sqrt(1.0 / alphas)
+    posterior_variance = betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
+    return {
+        "betas": betas,
+        "alphas_cumprod": alphas_cumprod,
+        "sqrt_alphas_cumprod": sqrt_alphas_cumprod,
+        "sqrt_one_minus": sqrt_one_minus,
+        "sqrt_recip_alphas": sqrt_recip_alphas,
+        "posterior_variance": posterior_variance,
+    }
diff --git a/tests/test_train_diffusion_arc.py b/tests/test_train_diffusion_arc.py
new file mode 100644
index 0000000..b29afed
--- /dev/null
+++ b/tests/test_train_diffusion_arc.py
@@ -0,0 +1,71 @@
+import json
+import sys
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+pytest.importorskip("torch")
+
+from train_diffusion_arc import main
+
+
+def test_main_runs_with_tiny_model(tmp_path: Path) -> None:
+    data_dir = tmp_path / "arc"
+    training_dir = data_dir / "training"
+    training_dir.mkdir(parents=True)
+
+    task_content = {
+        "train": [
+            {
+                "input": [[0, 1, 2], [2, 1, 0], [0, 0, 0]],
+                "output": [[1, 2, 0], [0, 1, 2], [2, 2, 2]],
+            },
+            {
+                "input": [[3, 3, 3], [3, 4, 3], [3, 3, 3]],
+                "output": [[4, 4, 4], [4, 5, 4], [4, 4, 4]],
+            },
+        ],
+        "test": [],
+    }
+    (training_dir / "task.json").write_text(json.dumps(task_content))
+
+    output_dir = tmp_path / "outputs"
+
+    argv = [
+        str(data_dir),
+        "--output-dir",
+        str(output_dir),
+        "--batch-size",
+        "1",
+        "--epochs",
+        "1",
+        "--timesteps",
+        "4",
+        "--num-workers",
+        "0",
+        "--log-interval",
+        "1",
+        "--skip-param-check",
+        "--device",
+        "cpu",
+        "--max-grid-size",
+        "3",
+        "--d-model",
+        "16",
+        "--num-heads",
+        "4",
+        "--num-layers",
+        "1",
+        "--dim-feedforward",
+        "32",
+        "--time-embed-dim",
+        "32",
+    ]
+
+    main(argv)
+
+    assert (output_dir / "final_model.pt").exists()
diff --git a/train_diffusion_arc.py b/train_diffusion_arc.py
new file mode 100644
index 0000000..bf30929
--- /dev/null
+++ b/train_diffusion_arc.py
@@ -0,0 +1,259 @@
+#!/usr/bin/env python3
+"""Train a diffusion transformer on ARC style tasks."""
+
+from __future__ import annotations
+
+import argparse
+import os
+from pathlib import Path
+from typing import Dict
+
+import torch
+from torch.utils.data import DataLoader, random_split
+
+from dllm import (
+    ARCTaskDataset,
+    DiffusionTransformer,
+    DiffusionTransformerConfig,
+    arc_collate,
+    build_diffusion_schedule,
+)
+
+
+def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("data_dir", type=str, help="Path to ARC dataset root directory")
+    parser.add_argument("--output-dir", type=str, default="outputs/diffusion_arc")
+    parser.add_argument("--batch-size", type=int, default=32)
+    parser.add_argument("--epochs", type=int, default=50)
+    parser.add_argument("--lr", type=float, default=3e-4)
+    parser.add_argument("--weight-decay", type=float, default=0.01)
+    parser.add_argument("--timesteps", type=int, default=1000)
+    parser.add_argument("--val-fraction", type=float, default=0.1)
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--grad-clip", type=float, default=1.0)
+    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
+    parser.add_argument("--ema", type=float, default=0.0, help="EMA decay for weights")
+    parser.add_argument("--duality-weight", type=float, default=0.5)
+    parser.add_argument("--log-interval", type=int, default=100)
+    parser.add_argument("--num-workers", type=int, default=2)
+    parser.add_argument("--save-interval", type=int, default=5)
+    parser.add_argument("--resume", type=str, default="", help="Resume checkpoint path")
+    parser.add_argument("--augment", action="store_true", help="Enable random flips for augmentation")
+    parser.add_argument("--mixed-precision", action="store_true")
+    parser.add_argument("--max-grid-size", type=int, default=30)
+    parser.add_argument("--d-model", type=int, default=288)
+    parser.add_argument("--num-heads", type=int, default=8)
+    parser.add_argument("--num-layers", type=int, default=7)
+    parser.add_argument("--dim-feedforward", type=int, default=1152)
+    parser.add_argument("--time-embed-dim", type=int, default=512)
+    parser.add_argument(
+        "--skip-param-check",
+        action="store_true",
+        help="Skip enforcing the ~7M parameter count. Useful for tests.",
+    )
+    return parser.parse_args(argv)
+
+
+def set_seed(seed: int) -> None:
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+
+
+def mask_mse(pred: torch.Tensor, target: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+    diff = (pred - target) * mask
+    mse = (diff ** 2).sum() / mask.sum().clamp(min=1.0)
+    return mse
+
+
+def to_device(batch: Dict[str, torch.Tensor], device: torch.device) -> Dict[str, torch.Tensor]:
+    return {k: v.to(device) for k, v in batch.items()}
+
+
+def decode_tokens(embeddings: torch.Tensor, token_embed: torch.nn.Embedding) -> torch.Tensor:
+    weight = token_embed.weight
+    logits = torch.einsum("bld,vd->blv", embeddings, weight)
+    tokens = logits.argmax(dim=-1)
+    return tokens
+
+
+def main(argv: list[str] | None = None) -> None:
+    args = parse_args(argv)
+    set_seed(args.seed)
+
+    device = torch.device(args.device)
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    dataset = ARCTaskDataset(
+        args.data_dir,
+        split="training",
+        max_grid_size=args.max_grid_size,
+        augment=args.augment,
+    )
+    val_size = max(1, int(len(dataset) * args.val_fraction))
+    train_size = len(dataset) - val_size
+    generator = torch.Generator().manual_seed(args.seed)
+    train_dataset, val_dataset = random_split(
+        dataset, [train_size, val_size], generator=generator
+    )
+
+    train_loader = DataLoader(
+        train_dataset,
+        batch_size=args.batch_size,
+        shuffle=True,
+        collate_fn=arc_collate,
+        num_workers=args.num_workers,
+        pin_memory=True,
+    )
+    val_loader = DataLoader(
+        val_dataset,
+        batch_size=args.batch_size,
+        shuffle=False,
+        collate_fn=arc_collate,
+        num_workers=args.num_workers,
+        pin_memory=True,
+    )
+
+    config = DiffusionTransformerConfig(
+        max_timesteps=args.timesteps,
+        max_grid_size=args.max_grid_size,
+        d_model=args.d_model,
+        num_heads=args.num_heads,
+        num_layers=args.num_layers,
+        dim_feedforward=args.dim_feedforward,
+        time_embed_dim=args.time_embed_dim,
+    )
+    model = DiffusionTransformer(config).to(device)
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Model parameters: {total_params/1e6:.2f}M")
+    if not args.skip_param_check and not (6.5e6 <= total_params <= 7.5e6):
+        raise RuntimeError("Model parameter count deviates from 7M target")
+
+    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
+    scaler = torch.cuda.amp.GradScaler(enabled=args.mixed_precision)
+
+    ema_model = None
+    if args.ema > 0:
+        ema_model = DiffusionTransformer(config).to(device)
+        ema_model.load_state_dict(model.state_dict())
+
+    if args.resume:
+        state = torch.load(args.resume, map_location=device)
+        model.load_state_dict(state["model"])
+        optimizer.load_state_dict(state["optimizer"])
+        scheduler.load_state_dict(state["scheduler"])
+        if ema_model is not None and "ema" in state:
+            ema_model.load_state_dict(state["ema"])
+        print(f"Resumed from {args.resume}")
+
+    schedule = build_diffusion_schedule(args.timesteps, device=device)
+
+    for epoch in range(1, args.epochs + 1):
+        model.train()
+        epoch_loss = 0.0
+        for step, batch in enumerate(train_loader, start=1):
+            batch = to_device(batch, device)
+            optimizer.zero_grad(set_to_none=True)
+            with torch.cuda.amp.autocast(enabled=args.mixed_precision):
+                loss = compute_loss(model, batch, schedule, args.duality_weight)
+            scaler.scale(loss).backward()
+            if args.grad_clip > 0:
+                scaler.unscale_(optimizer)
+                torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
+            scaler.step(optimizer)
+            scaler.update()
+            epoch_loss += loss.item()
+            if args.ema > 0:
+                update_ema(model, ema_model, args.ema)
+            if step % args.log_interval == 0:
+                print(f"Epoch {epoch} Step {step}: loss={loss.item():.4f}")
+        scheduler.step()
+        avg_loss = epoch_loss / max(1, len(train_loader))
+        val_loss = evaluate(model, val_loader, schedule, args.duality_weight, device)
+        print(f"Epoch {epoch}: train_loss={avg_loss:.4f} val_loss={val_loss:.4f}")
+        if epoch % args.save_interval == 0:
+            save_path = Path(args.output_dir) / f"checkpoint_{epoch}.pt"
+            save_checkpoint(model, optimizer, scheduler, ema_model, save_path)
+            print(f"Saved checkpoint to {save_path}")
+
+    final_path = Path(args.output_dir) / "final_model.pt"
+    save_checkpoint(model, optimizer, scheduler, ema_model, final_path)
+    print(f"Training completed, model saved to {final_path}")
+
+
+@torch.no_grad()
+def evaluate(
+    model: DiffusionTransformer,
+    loader: DataLoader,
+    schedule: Dict[str, torch.Tensor],
+    duality_weight: float,
+    device: torch.device,
+) -> float:
+    model.eval()
+    losses = []
+    for batch in loader:
+        batch = to_device(batch, device)
+        loss = compute_loss(model, batch, schedule, duality_weight)
+        losses.append(loss.item())
+    return sum(losses) / max(1, len(losses))
+
+
+def compute_loss(
+    model: DiffusionTransformer,
+    batch: Dict[str, torch.Tensor],
+    schedule: Dict[str, torch.Tensor],
+    duality_weight: float,
+) -> torch.Tensor:
+    target_tokens = batch["target"]
+    target_mask = batch["target_mask"].unsqueeze(-1)
+    cond_tokens = batch["condition"]
+    cond_mask = batch["condition_mask"]
+    cfg = model.config
+
+    timesteps = torch.randint(0, cfg.max_timesteps, (target_tokens.size(0),), device=target_tokens.device)
+    target_embed = model.token_embed(target_tokens) * target_mask
+    noise = torch.randn_like(target_embed)
+    sqrt_alphas_cumprod = schedule["sqrt_alphas_cumprod"][timesteps].view(-1, 1, 1)
+    sqrt_one_minus = schedule["sqrt_one_minus"][timesteps].view(-1, 1, 1)
+    noisy = sqrt_alphas_cumprod * target_embed + sqrt_one_minus * noise
+    pred_noise = model(noisy, cond_tokens, cond_mask, batch["target_mask"], timesteps)
+    weighted_mask = target_mask
+    noise_loss = mask_mse(pred_noise, noise, weighted_mask)
+    if duality_weight > 0:
+        pred_x0 = (noisy - sqrt_one_minus * pred_noise) / sqrt_alphas_cumprod
+        clean_loss = mask_mse(pred_x0, target_embed, weighted_mask)
+        return noise_loss + duality_weight * clean_loss
+    return noise_loss
+
+
+def update_ema(model: DiffusionTransformer, ema: DiffusionTransformer, decay: float) -> None:
+    with torch.no_grad():
+        msd = model.state_dict()
+        for k, v in ema.state_dict().items():
+            if v.dtype.is_floating_point:
+                v.mul_(decay).add_(msd[k], alpha=1 - decay)
+            else:
+                v.copy_(msd[k])
+
+
+def save_checkpoint(
+    model: DiffusionTransformer,
+    optimizer: torch.optim.Optimizer,
+    scheduler: torch.optim.lr_scheduler._LRScheduler,
+    ema_model: DiffusionTransformer | None,
+    path: Path,
+) -> None:
+    payload = {
+        "model": model.state_dict(),
+        "optimizer": optimizer.state_dict(),
+        "scheduler": scheduler.state_dict(),
+    }
+    if ema_model is not None:
+        payload["ema"] = ema_model.state_dict()
+    torch.save(payload, path)
+
+
+if __name__ == "__main__":
+    main()

From 5400828736750408b9d24393854b9556a724abe1 Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 12:01:21 +0100
Subject: [PATCH 02/13] Document ARC dataset source and usage

---
 README.md              | 29 +++++++++++++++++++++++++++++
 dllm/arc_dataset.py    |  8 +++++++-
 train_diffusion_arc.py | 10 +++++++++-
 3 files changed, 45 insertions(+), 2 deletions(-)
 create mode 100644 README.md

diff --git a/README.md b/README.md
new file mode 100644
index 0000000..15bdd6c
--- /dev/null
+++ b/README.md
@@ -0,0 +1,29 @@
+# Diffusion Transformer for ARC
+
+This repository contains an experimental diffusion transformer training pipeline for ARC-AGI style reasoning tasks.
+
+## Dataset
+
+The training script expects the canonical ARC task JSON files from the official [fchollet/ARC](https://github.com/fchollet/ARC) repository. Download or clone that repository and point the training command at the `data` directory inside it, which contains the `training/` and `evaluation/` folders:
+
+```bash
+# Example setup
+mkdir -p data
+cd data
+curl -L https://github.com/fchollet/ARC/archive/refs/heads/master.zip -o arc.zip
+unzip arc.zip 'ARC-master/data/*'
+cd ..
+
+# Run training
+python train_diffusion_arc.py data/ARC-master/data
+```
+
+Any mirror with the same folder structure will also work. The `ARCTaskDataset` loader simply walks every `*.json` file inside the specified split directory.
+
+## Tests
+
+A minimal CPU smoke test is available via:
+
+```bash
+pytest tests/test_train_diffusion_arc.py -k tiny_cpu
+```
diff --git a/dllm/arc_dataset.py b/dllm/arc_dataset.py
index bd2e848..9138b41 100644
--- a/dllm/arc_dataset.py
+++ b/dllm/arc_dataset.py
@@ -1,4 +1,10 @@
-"""Utilities for loading ARC-AGI style datasets."""
+"""Utilities for loading ARC-AGI style datasets.
+
+The loader expects the canonical ARC directory structure from the
+`fchollet/ARC <https://github.com/fchollet/ARC>`_ repository (or an
+equivalent mirror). ``root`` should point at the folder that contains the
+``training`` and ``evaluation`` sub-directories with ``*.json`` task files.
+"""
 
 from __future__ import annotations
 
diff --git a/train_diffusion_arc.py b/train_diffusion_arc.py
index bf30929..9d41416 100644
--- a/train_diffusion_arc.py
+++ b/train_diffusion_arc.py
@@ -22,7 +22,15 @@
 
 def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
     parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("data_dir", type=str, help="Path to ARC dataset root directory")
+    parser.add_argument(
+        "data_dir",
+        type=str,
+        help=(
+            "Path to ARC dataset root directory (the folder containing the "
+            "'training' and 'evaluation' sub-directories from the official "
+            "fchollet/ARC data dump)."
+        ),
+    )
     parser.add_argument("--output-dir", type=str, default="outputs/diffusion_arc")
     parser.add_argument("--batch-size", type=int, default=32)
     parser.add_argument("--epochs", type=int, default=50)

From c72f1ade87ad405b99368d2b6505a142ae026670 Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 11:24:25 +0000
Subject: [PATCH 03/13] fixes

---
 dllm/diffusion_transformer.py | 2 +-
 train_diffusion_arc.py        | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/dllm/diffusion_transformer.py b/dllm/diffusion_transformer.py
index d88db38..7eb1ea3 100644
--- a/dllm/diffusion_transformer.py
+++ b/dllm/diffusion_transformer.py
@@ -156,7 +156,7 @@ def sample(
 
 def timestep_embedding(timesteps: torch.Tensor, dim: int, max_period: int = 10000) -> torch.Tensor:
     half = dim // 2
-    freqs = torch.exp(-math.log(max_period) * torch.arange(half, dtype=torch.float32) / half)
+    freqs = torch.exp(-math.log(max_period) * torch.arange(half, dtype=torch.float32, device=timesteps.device) / half)
     args = timesteps.float().unsqueeze(1) * freqs.unsqueeze(0)
     embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
     if dim % 2:
diff --git a/train_diffusion_arc.py b/train_diffusion_arc.py
index 9d41416..f9f7536 100644
--- a/train_diffusion_arc.py
+++ b/train_diffusion_arc.py
@@ -103,7 +103,7 @@ def main(argv: list[str] | None = None) -> None:
     train_size = len(dataset) - val_size
     generator = torch.Generator().manual_seed(args.seed)
     train_dataset, val_dataset = random_split(
-        dataset, [train_size, val_size], generator=generator
+        dataset, [train_size, val_size], generator=generator,
     )
 
     train_loader = DataLoader(
@@ -135,8 +135,8 @@ def main(argv: list[str] | None = None) -> None:
     model = DiffusionTransformer(config).to(device)
     total_params = sum(p.numel() for p in model.parameters())
     print(f"Model parameters: {total_params/1e6:.2f}M")
-    if not args.skip_param_check and not (6.5e6 <= total_params <= 7.5e6):
-        raise RuntimeError("Model parameter count deviates from 7M target")
+    #if not args.skip_param_check and not (6.5e6 <= total_params <= 7.5e6):
+    #    raise RuntimeError("Model parameter count deviates from 7M target")
 
     optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
     scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)

From 288dc743dfe134447cbee29079ebb93494c09fe8 Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 12:48:16 +0100
Subject: [PATCH 04/13] Add batch visualization utilities for ARC diffusion

---
 README.md                  |  10 ++
 batch_visualization.py     | 155 ++++++++++++++++++++++++++++
 dllm/__init__.py           |   8 ++
 dllm/visualize_sampling.py | 203 +++++++++++++++++++++++++++++++++++++
 train_diffusion_arc.py     |   7 --
 5 files changed, 376 insertions(+), 7 deletions(-)
 create mode 100644 batch_visualization.py
 create mode 100644 dllm/visualize_sampling.py

diff --git a/README.md b/README.md
index 15bdd6c..8743fdf 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,16 @@ python train_diffusion_arc.py data/ARC-master/data
 
 Any mirror with the same folder structure will also work. The `ARCTaskDataset` loader simply walks every `*.json` file inside the specified split directory.
 
+## Visualization
+
+To inspect the batches used during training, including how the diffusion process corrupts the targets, run the visualization helper:
+
+```bash
+python batch_visualization.py data/ARC-master/data --checkpoint outputs/diffusion_arc/final_model.pt
+```
+
+This command saves `train_batches.png` and `val_batches.png` under `outputs/visualizations/`, each showing five batches of samples with the condition, target, and a randomly corrupted view at different diffusion timesteps.
+
 ## Tests
 
 A minimal CPU smoke test is available via:
diff --git a/batch_visualization.py b/batch_visualization.py
new file mode 100644
index 0000000..31a9216
--- /dev/null
+++ b/batch_visualization.py
@@ -0,0 +1,155 @@
+#!/usr/bin/env python3
+"""Generate batch visualizations for ARC train/validation splits."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+from typing import List
+
+import matplotlib.pyplot as plt
+import torch
+from torch.utils.data import DataLoader, random_split
+
+from dllm import (
+    ARCTaskDataset,
+    DiffusionTransformer,
+    DiffusionTransformerConfig,
+    arc_collate,
+    build_diffusion_schedule,
+    create_batch_visualization,
+)
+
+
+def parse_args(argv: List[str] | None = None) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "data_dir",
+        type=str,
+        help="Path to the ARC dataset root containing the 'training' folder.",
+    )
+    parser.add_argument("--output-dir", type=str, default="outputs/visualizations")
+    parser.add_argument("--batch-size", type=int, default=8)
+    parser.add_argument("--num-batches", type=int, default=5)
+    parser.add_argument("--examples-per-batch", type=int, default=1)
+    parser.add_argument("--val-fraction", type=float, default=0.1)
+    parser.add_argument("--max-grid-size", type=int, default=30)
+    parser.add_argument("--timesteps", type=int, default=1000)
+    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--d-model", type=int, default=288)
+    parser.add_argument("--num-heads", type=int, default=8)
+    parser.add_argument("--num-layers", type=int, default=7)
+    parser.add_argument("--dim-feedforward", type=int, default=1152)
+    parser.add_argument("--time-embed-dim", type=int, default=512)
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        default="",
+        help="Optional path to a checkpoint produced by train_diffusion_arc.py.",
+    )
+    return parser.parse_args(argv)
+
+
+def set_seed(seed: int) -> None:
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+
+
+def _gather_batches(loader: DataLoader, count: int) -> List[dict[str, torch.Tensor]]:
+    batches: List[dict[str, torch.Tensor]] = []
+    for batch in loader:
+        batches.append(batch)
+        if len(batches) >= count:
+            break
+    return batches
+
+
+def main(argv: List[str] | None = None) -> None:
+    args = parse_args(argv)
+    set_seed(args.seed)
+
+    device = torch.device(args.device)
+
+    dataset = ARCTaskDataset(
+        args.data_dir,
+        split="training",
+        max_grid_size=args.max_grid_size,
+    )
+    val_size = max(1, int(len(dataset) * args.val_fraction))
+    train_size = len(dataset) - val_size
+    generator = torch.Generator().manual_seed(args.seed)
+    train_dataset, val_dataset = random_split(
+        dataset,
+        [train_size, val_size],
+        generator=generator,
+    )
+
+    train_loader = DataLoader(
+        train_dataset,
+        batch_size=args.batch_size,
+        shuffle=True,
+        collate_fn=arc_collate,
+        num_workers=0,
+    )
+    val_loader = DataLoader(
+        val_dataset,
+        batch_size=args.batch_size,
+        shuffle=False,
+        collate_fn=arc_collate,
+        num_workers=0,
+    )
+
+    config = DiffusionTransformerConfig(
+        max_timesteps=args.timesteps,
+        max_grid_size=args.max_grid_size,
+        d_model=args.d_model,
+        num_heads=args.num_heads,
+        num_layers=args.num_layers,
+        dim_feedforward=args.dim_feedforward,
+        time_embed_dim=args.time_embed_dim,
+    )
+    model = DiffusionTransformer(config).to(device)
+    if args.checkpoint:
+        state = torch.load(args.checkpoint, map_location=device)
+        model.load_state_dict(state["model"])
+
+    schedule = build_diffusion_schedule(args.timesteps, device=device)
+
+    train_batches = _gather_batches(train_loader, args.num_batches)
+    val_batches = _gather_batches(val_loader, args.num_batches)
+
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    train_fig = create_batch_visualization(
+        train_batches,
+        model=model,
+        diffusion_schedule=schedule,
+        max_grid_size=args.max_grid_size,
+        examples_per_batch=args.examples_per_batch,
+        title="Training batches",
+    )
+    train_path = output_dir / "train_batches.png"
+    train_fig.savefig(train_path, bbox_inches="tight")
+    plt.close(train_fig)
+
+    val_fig = create_batch_visualization(
+        val_batches,
+        model=model,
+        diffusion_schedule=schedule,
+        max_grid_size=args.max_grid_size,
+        examples_per_batch=args.examples_per_batch,
+        title="Validation batches",
+    )
+    val_path = output_dir / "val_batches.png"
+    val_fig.savefig(val_path, bbox_inches="tight")
+    plt.close(val_fig)
+
+    print(f"Saved training visualization to {train_path}")
+    print(f"Saved validation visualization to {val_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/dllm/__init__.py b/dllm/__init__.py
index f275b69..2c95a5d 100644
--- a/dllm/__init__.py
+++ b/dllm/__init__.py
@@ -8,6 +8,11 @@
     build_diffusion_schedule,
     timestep_embedding,
 )
+from .visualize_sampling import (
+    ExampleVisualization,
+    create_batch_visualization,
+    decode_tokens,
+)
 
 __all__ = [
     "ARCTaskDataset",
@@ -17,4 +22,7 @@
     "cosine_beta_schedule",
     "build_diffusion_schedule",
     "timestep_embedding",
+    "ExampleVisualization",
+    "create_batch_visualization",
+    "decode_tokens",
 ]
diff --git a/dllm/visualize_sampling.py b/dllm/visualize_sampling.py
new file mode 100644
index 0000000..50281be
--- /dev/null
+++ b/dllm/visualize_sampling.py
@@ -0,0 +1,203 @@
+"""Utilities for visualizing ARC batches and diffusion corruption."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Dict, List, Sequence
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+from .diffusion_transformer import DiffusionTransformer
+
+# ARC uses 10 colors (0-9). Index 10 is reserved for padding/empty cells.
+# The palette below mirrors the canonical ARC visualization colors.
+_COLOR_PALETTE = np.array(
+    [
+        (0, 0, 0),  # 0 - black
+        (0, 119, 187),  # 1 - blue
+        (221, 95, 0),  # 2 - orange
+        (0, 158, 115),  # 3 - green
+        (204, 204, 0),  # 4 - yellow
+        (148, 0, 211),  # 5 - purple
+        (255, 105, 180),  # 6 - pink
+        (0, 191, 196),  # 7 - cyan
+        (255, 0, 0),  # 8 - red
+        (128, 128, 128),  # 9 - gray
+        (255, 255, 255),  # 10 - white / padding
+    ],
+    dtype=np.float32,
+) / 255.0
+
+
+@dataclass
+class ExampleVisualization:
+    """Container for a single visualization example."""
+
+    condition: torch.Tensor
+    condition_mask: torch.Tensor
+    target: torch.Tensor
+    target_mask: torch.Tensor
+    corrupted: torch.Tensor
+    timestep: int
+    batch_index: int
+    example_index: int
+
+
+def decode_tokens(embeddings: torch.Tensor, token_embed: torch.nn.Embedding) -> torch.Tensor:
+    """Map embedding vectors back to discrete token ids via nearest embedding."""
+
+    weight = token_embed.weight
+    logits = torch.einsum("bld,vd->blv", embeddings, weight)
+    tokens = logits.argmax(dim=-1)
+    return tokens
+
+
+def _tokens_to_color_grid(
+    tokens: torch.Tensor,
+    mask: torch.Tensor,
+    *,
+    max_grid_size: int,
+    pad_token_id: int = 10,
+) -> np.ndarray:
+    """Convert a flattened token grid into an RGB image using the ARC palette."""
+
+    grid_tokens = tokens.view(max_grid_size, max_grid_size).cpu().numpy()
+    grid_mask = mask.view(max_grid_size, max_grid_size).cpu().numpy()
+    painted = np.full_like(grid_tokens, fill_value=pad_token_id)
+    painted[grid_mask > 0.5] = grid_tokens[grid_mask > 0.5]
+    return _COLOR_PALETTE[painted]
+
+
+def _plot_single(ax: plt.Axes, grid: np.ndarray, title: str) -> None:
+    ax.imshow(grid, interpolation="nearest")
+    ax.set_title(title)
+    ax.set_xticks([])
+    ax.set_yticks([])
+    ax.grid(False)
+
+
+def _collect_examples(
+    batches: Sequence[Dict[str, torch.Tensor]],
+    *,
+    model: DiffusionTransformer,
+    diffusion_schedule: Dict[str, torch.Tensor],
+    max_examples_per_batch: int,
+) -> List[ExampleVisualization]:
+    device = next(model.parameters()).device
+    examples: List[ExampleVisualization] = []
+
+    sqrt_alphas_cumprod = diffusion_schedule["sqrt_alphas_cumprod"]
+    sqrt_one_minus = diffusion_schedule["sqrt_one_minus"]
+
+    for batch_index, batch in enumerate(batches):
+        condition = batch["condition"].to(device)
+        condition_mask = batch["condition_mask"].to(device)
+        target = batch["target"].to(device)
+        target_mask = batch["target_mask"].to(device)
+
+        batch_size = target.size(0)
+        take = min(max_examples_per_batch, batch_size)
+        if take == 0:
+            continue
+
+        timesteps = torch.randint(
+            low=0,
+            high=model.config.max_timesteps,
+            size=(batch_size,),
+            device=device,
+        )
+        target_embed = model.token_embed(target) * target_mask.unsqueeze(-1)
+        noise = torch.randn_like(target_embed)
+        sqrt_alpha = sqrt_alphas_cumprod[timesteps].view(-1, 1, 1)
+        sqrt_one = sqrt_one_minus[timesteps].view(-1, 1, 1)
+        noisy = sqrt_alpha * target_embed + sqrt_one * noise
+        corrupted_tokens = decode_tokens(noisy, model.token_embed)
+
+        for example_index in range(take):
+            examples.append(
+                ExampleVisualization(
+                    condition=condition[example_index].cpu(),
+                    condition_mask=condition_mask[example_index].cpu(),
+                    target=target[example_index].cpu(),
+                    target_mask=target_mask[example_index].cpu(),
+                    corrupted=corrupted_tokens[example_index].cpu(),
+                    timestep=int(timesteps[example_index].item()),
+                    batch_index=batch_index,
+                    example_index=example_index,
+                )
+            )
+    return examples
+
+
+def create_batch_visualization(
+    batches: Sequence[Dict[str, torch.Tensor]],
+    *,
+    model: DiffusionTransformer,
+    diffusion_schedule: Dict[str, torch.Tensor],
+    max_grid_size: int,
+    examples_per_batch: int = 1,
+    title: str | None = None,
+) -> plt.Figure:
+    """Create a matplotlib figure visualizing dataset batches and corruption."""
+
+    examples = _collect_examples(
+        batches,
+        model=model,
+        diffusion_schedule=diffusion_schedule,
+        max_examples_per_batch=examples_per_batch,
+    )
+    if not examples:
+        raise ValueError("No examples available to visualize.")
+
+    n_rows = len(examples)
+    fig, axes = plt.subplots(n_rows, 3, figsize=(9, max(3, 3 * n_rows / 2)))
+    if title:
+        fig.suptitle(title, fontsize=16)
+
+    if n_rows == 1:
+        axes = np.expand_dims(axes, axis=0)
+
+    for row, example in enumerate(examples):
+        row_axes = axes[row]
+        cond_grid = _tokens_to_color_grid(
+            example.condition,
+            example.condition_mask,
+            max_grid_size=max_grid_size,
+        )
+        tgt_grid = _tokens_to_color_grid(
+            example.target,
+            example.target_mask,
+            max_grid_size=max_grid_size,
+        )
+        corrupted_grid = _tokens_to_color_grid(
+            example.corrupted,
+            example.target_mask,
+            max_grid_size=max_grid_size,
+        )
+
+        _plot_single(
+            row_axes[0],
+            cond_grid,
+            title=f"Batch {example.batch_index + 1} Cond #{example.example_index + 1}",
+        )
+        _plot_single(row_axes[1], tgt_grid, title="Target")
+        _plot_single(
+            row_axes[2],
+            corrupted_grid,
+            title=f"Corrupted (t={example.timestep})",
+        )
+
+    if title:
+        fig.tight_layout(rect=(0, 0, 1, 0.96))
+    else:
+        fig.tight_layout()
+    return fig
+
+
+__all__ = [
+    "ExampleVisualization",
+    "create_batch_visualization",
+    "decode_tokens",
+]
diff --git a/train_diffusion_arc.py b/train_diffusion_arc.py
index f9f7536..4d9d269 100644
--- a/train_diffusion_arc.py
+++ b/train_diffusion_arc.py
@@ -79,13 +79,6 @@ def to_device(batch: Dict[str, torch.Tensor], device: torch.device) -> Dict[str,
     return {k: v.to(device) for k, v in batch.items()}
 
 
-def decode_tokens(embeddings: torch.Tensor, token_embed: torch.nn.Embedding) -> torch.Tensor:
-    weight = token_embed.weight
-    logits = torch.einsum("bld,vd->blv", embeddings, weight)
-    tokens = logits.argmax(dim=-1)
-    return tokens
-
-
 def main(argv: list[str] | None = None) -> None:
     args = parse_args(argv)
     set_seed(args.seed)

From b89be3d1124d4fedba2b54675324d741e912c459 Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 12:58:55 +0100
Subject: [PATCH 05/13] Document safer ARC augmentation ideas

---
 docs/arc_dataset.md | 146 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 146 insertions(+)
 create mode 100644 docs/arc_dataset.md

diff --git a/docs/arc_dataset.md b/docs/arc_dataset.md
new file mode 100644
index 0000000..a4dad7a
--- /dev/null
+++ b/docs/arc_dataset.md
@@ -0,0 +1,146 @@
+# ARC Dataset, DataLoader, and Known Problems
+
+This document explains how the project loads ARC-AGI style tasks, how the
+`torch.utils.data.Dataset` and `DataLoader` are configured, what tensors are
+contained in each training batch, and the main problems with the current
+implementation.
+
+## Directory structure and input format
+
+The dataset utilities expect the canonical directory layout distributed in the
+[`fchollet/ARC`](https://github.com/fchollet/ARC) repository. When you download
+that dataset the root directory contains the sub-folders:
+
+```
+<root>/training/
+<root>/evaluation/
+```
+
+Each sub-folder stores multiple `*.json` task files. Every file contains a list
+of training examples under the `"train"` key (the original ARC format also
+provides a `"test"` list, which we do not consume during model training).
+
+Within the JSON file each entry inside `"train"` is a dictionary with `"input"`
+and `"output"` fields. Each field is a 2-D list of integers representing a color
+grid. The integers fall in the range `[0, 9]` for the ten canonical ARC colors.
+
+## `ARCTaskDataset`
+
+[`dllm/arc_dataset.py`](../dllm/arc_dataset.py) defines the
+`ARCTaskDataset` class, which inherits from `torch.utils.data.Dataset`.
+Key behaviors:
+
+* **Initialization** – the constructor walks the chosen `training` or
+  `evaluation` split directory and loads every JSON file. For every pair inside
+  the `"train"` list the dataset stores an `ARCExample` dataclass with
+  `input_grid` and `output_grid` attributes.【F:dllm/arc_dataset.py†L39-L73】
+* **Grid padding** – ARC tasks contain grids of varying size. Before they can
+  be fed to the model, each grid is padded to a fixed `max_grid_size ×
+  max_grid_size` square (default 30×30). Padding is handled by the private
+  `_pad_grid` helper, which returns both the flattened token tensor and a mask
+  that marks real (value `1.0`) versus padded (value `0.0`) cells. The padding
+  token defaults to `10`, which lies outside the normal color range so models
+  can distinguish padding from real pixels.【F:dllm/arc_dataset.py†L22-L63】
+* **Samples** – calling `dataset[idx]` yields a dictionary with four keys:
+  `"condition"`, `"condition_mask"`, `"target"`, and `"target_mask"`. Each is a
+  1-D tensor of length `max_grid_size ** 2`. `condition` and `condition_mask`
+  correspond to the example’s input grid, while `target` and `target_mask`
+  describe the desired output grid. When `augment=True`, random horizontal and
+  vertical flips are applied to both grids (and masks) with independent
+  probability `0.5` each.【F:dllm/arc_dataset.py†L65-L116】
+
+The dataset’s length equals the number of `train` pairs found across every JSON
+file in the selected split. Importantly, ARC refers to each JSON file as a
+single *task* that bundles several input/output demonstrations. The
+`ARCTaskDataset` flattens those demonstrations so that every individual
+`{"input": ..., "output": ...}` pair becomes its own dataset element. When a
+`DataLoader` batches items together (often with `shuffle=True`), the batch may
+contain examples originating from many different tasks. There is no special
+grouping to keep demonstrations from the same task adjacent, because the
+current training objective treats every demonstration independently.
+
+## Collation and DataLoader configuration
+
+Training scripts construct PyTorch `DataLoader` instances using the custom
+`arc_collate` function defined alongside the dataset class.
+
+* **`arc_collate`** – this function receives a list of per-item dictionaries and
+  stacks the `condition`, `condition_mask`, `target`, and `target_mask` tensors
+  into batched tensors with shape `(batch_size, max_grid_size**2)`. The output
+  is a dictionary with the same four keys expected by the model.【F:dllm/arc_dataset.py†L118-L128】
+* **`DataLoader` setup** – for example, `train_diffusion_arc.py` creates the
+  dataset, randomly splits it into training and validation subsets, and then
+  wraps them with `DataLoader` objects that specify:
+  * `collate_fn=arc_collate`
+  * `shuffle=True` for the training loader and `False` for validation
+  * `batch_size` configured from the command-line (default `32`)
+  * `num_workers` and `pin_memory` tuned for efficient GPU feeding.【F:train_diffusion_arc.py†L69-L115】
+
+## Batch contents
+
+Each batch produced by the `DataLoader` is a dictionary with four entries:
+
+| Key                | Shape                            | DType           | Description                                                              |
+| ------------------ | -------------------------------- | --------------- | ------------------------------------------------------------------------ |
+| `"condition"`      | `(batch_size, max_grid_size**2)` | `torch.long`    | Flattened input grid tokens with padding tokens (`10`) filling leftovers. |
+| `"condition_mask"` | `(batch_size, max_grid_size**2)` | `torch.float32` | Binary mask (1.0 where the input grid is real, 0.0 on padding).           |
+| `"target"`         | `(batch_size, max_grid_size**2)` | `torch.long`    | Flattened output grid tokens padded to the same length.                   |
+| `"target_mask"`    | `(batch_size, max_grid_size**2)` | `torch.float32` | Binary mask for the output grid, matching the padding pattern.            |
+
+You can move the entire batch to a device using a simple comprehension, as done
+in the training script’s `to_device` helper.【F:train_diffusion_arc.py†L57-L64】
+
+These tensors supply the diffusion transformer with both the conditioning input
+and the desired target, while the masks allow the loss function to ignore padded
+cells when computing reconstruction errors.
+
+## Known problems
+
+Although the sections above describe the intended pipeline, several issues in
+the current codebase prevent the ARC loader from matching the canonical task
+structure and modelling objective.
+
+### Tasks are flattened into unrelated examples
+
+`ARCTaskDataset` loads every `{"input", "output"}` pair independently and stores
+them as separate items in the `examples` list.【F:dllm/arc_dataset.py†L44-L63】 In
+effect, the dataset breaks the ARC convention that all demonstrations belonging
+to a task should be seen together. When the training script later shuffles the
+dataset and slices it with `random_split`, individual demonstrations from the
+same task can land in different batches, and even in different train/validation
+splits.【F:train_diffusion_arc.py†L74-L105】 This destroys the contextual signal
+that ARC solvers rely on (observing multiple demonstrations before producing an
+answer for a held-out input), and it introduces leakage where the validation set
+may still expose partial information from training tasks.
+
+### Data augmentation corrupts the provided grids
+
+When the `--augment` flag is enabled, `_augment` performs random horizontal and
+vertical flips on both the input (`condition`) and the output (`target`) grids in
+each sample.【F:dllm/arc_dataset.py†L86-L116】 ARC demonstrations are carefully
+constructed; transforming the input grid changes the puzzle itself and can make
+the paired output meaningless. Because the goal is to generate the output grid
+given an unmodified input, these flips effectively corrupt the supervision
+signal by altering the examples that should remain fixed.
+
+A safer strategy would be to apply the **same** augmentation to every
+demonstration belonging to a task so that relative relationships stay intact, or
+to restrict augmentation to the generated output while leaving the conditioning
+input untouched. Another promising idea is to treat all demonstrations in a task
+as a candidate target: for a task that ships four examples, the loader could
+pick one of the demonstrations as the "output" and repurpose the remaining three
+as conditioning inputs, cycling this choice across epochs. Either approach would
+respect the intent of ARC tasks while still expanding the variety of supervision
+the model sees.
+
+### Diffusion objective ignores task-specific geometry
+
+During training, `compute_loss` embeds the target tokens, applies Gaussian noise,
+and asks the model to predict that noise.【F:train_diffusion_arc.py†L187-L235】
+While this is standard for diffusion models, the implementation does not supply
+the true target mask to the sampler: `DiffusionTransformer.sample` always
+constructs an all-ones `target_mask`, forcing the model to denoise a full
+30×30 grid regardless of the original puzzle size.【F:dllm/diffusion_transformer.py†L120-L160】
+Consequently the network must learn to hallucinate outputs for padded regions
+that should remain unused, and the sampling procedure cannot take advantage of
+the sparsity information available in the dataset.

From c9a8674ecb0f5afaa4a12525a008ae59e6b65a34 Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 13:01:19 +0100
Subject: [PATCH 06/13] Limit diffusion visualization timesteps to 0-99

---
 README.md                     | 2 +-
 batch_visualization.py        | 2 +-
 dllm/diffusion_transformer.py | 2 +-
 train_diffusion_arc.py        | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 8743fdf..ce2e9ab 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,7 @@ To inspect the batches used during training, including how the diffusion process
 python batch_visualization.py data/ARC-master/data --checkpoint outputs/diffusion_arc/final_model.pt
 ```
 
-This command saves `train_batches.png` and `val_batches.png` under `outputs/visualizations/`, each showing five batches of samples with the condition, target, and a randomly corrupted view at different diffusion timesteps.
+This command saves `train_batches.png` and `val_batches.png` under `outputs/visualizations/`, each showing five batches of samples with the condition, target, and a randomly corrupted view at different diffusion timesteps (defaulting to a compact 0–99 range).
 
 ## Tests
 
diff --git a/batch_visualization.py b/batch_visualization.py
index 31a9216..cdf5a3b 100644
--- a/batch_visualization.py
+++ b/batch_visualization.py
@@ -34,7 +34,7 @@ def parse_args(argv: List[str] | None = None) -> argparse.Namespace:
     parser.add_argument("--examples-per-batch", type=int, default=1)
     parser.add_argument("--val-fraction", type=float, default=0.1)
     parser.add_argument("--max-grid-size", type=int, default=30)
-    parser.add_argument("--timesteps", type=int, default=1000)
+    parser.add_argument("--timesteps", type=int, default=100)
     parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
     parser.add_argument("--seed", type=int, default=42)
     parser.add_argument("--d-model", type=int, default=288)
diff --git a/dllm/diffusion_transformer.py b/dllm/diffusion_transformer.py
index 7eb1ea3..91719ee 100644
--- a/dllm/diffusion_transformer.py
+++ b/dllm/diffusion_transformer.py
@@ -31,7 +31,7 @@ class DiffusionTransformerConfig:
     num_layers: int = 7
     dim_feedforward: int = 1152
     dropout: float = 0.1
-    max_timesteps: int = 1000
+    max_timesteps: int = 100
     time_embed_dim: int = 512
 
     @property
diff --git a/train_diffusion_arc.py b/train_diffusion_arc.py
index 4d9d269..b9aa95d 100644
--- a/train_diffusion_arc.py
+++ b/train_diffusion_arc.py
@@ -36,7 +36,7 @@ def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
     parser.add_argument("--epochs", type=int, default=50)
     parser.add_argument("--lr", type=float, default=3e-4)
     parser.add_argument("--weight-decay", type=float, default=0.01)
-    parser.add_argument("--timesteps", type=int, default=1000)
+    parser.add_argument("--timesteps", type=int, default=100)
     parser.add_argument("--val-fraction", type=float, default=0.1)
     parser.add_argument("--seed", type=int, default=42)
     parser.add_argument("--grad-clip", type=float, default=1.0)

From 8f2581323ed30d1e11b4875fe8ad69afb3d6639a Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 12:02:49 +0000
Subject: [PATCH 07/13] make name changes

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index ce2e9ab..d045eab 100644
--- a/README.md
+++ b/README.md
@@ -11,11 +11,11 @@ The training script expects the canonical ARC task JSON files from the official
 mkdir -p data
 cd data
 curl -L https://github.com/fchollet/ARC/archive/refs/heads/master.zip -o arc.zip
-unzip arc.zip 'ARC-master/data/*'
+unzip arc.zip 'ARC-AGI-master/data/*'
 cd ..
 
 # Run training
-python train_diffusion_arc.py data/ARC-master/data
+python train_diffusion_arc.py data/ARC-AGI-master/data
 ```
 
 Any mirror with the same folder structure will also work. The `ARCTaskDataset` loader simply walks every `*.json` file inside the specified split directory.
@@ -25,7 +25,7 @@ Any mirror with the same folder structure will also work. The `ARCTaskDataset` l
 To inspect the batches used during training, including how the diffusion process corrupts the targets, run the visualization helper:
 
 ```bash
-python batch_visualization.py data/ARC-master/data --checkpoint outputs/diffusion_arc/final_model.pt
+python batch_visualization.py data/ARC-AGI-master/data --checkpoint outputs/diffusion_arc/final_model.pt
 ```
 
 This command saves `train_batches.png` and `val_batches.png` under `outputs/visualizations/`, each showing five batches of samples with the condition, target, and a randomly corrupted view at different diffusion timesteps (defaulting to a compact 0–99 range).

From b23458e11514d8138b9a059e6c1b8272a6a28650 Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 12:03:21 +0000
Subject: [PATCH 08/13] x

---
 .gitignore | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..0d397a6
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,5 @@
+__pycache__/
+data/
+dllm/__pycache__/
+outputs/
+tests/__pycache__/

From 5877ab77c215279f941860bd9808846ce6d3cbea Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 14:14:01 +0000
Subject: [PATCH 09/13] more

---
 .python-version                             |    1 +
 batch_visualization.py                      |  155 ---
 dllm/visualize_sampling.py                  |  203 ----
 pyproject.toml                              |   13 +
 sample_diffusion_arc.py                     |  352 ++++++
 {dllm => src/dllm}/__init__.py              |    4 +-
 {dllm => src/dllm}/arc_dataset.py           |    0
 {dllm => src/dllm}/diffusion_transformer.py |   49 +-
 src/dllm/visualize_sampling.py              |  369 ++++++
 tests/test_corruption_visualization.py      |   63 ++
 train_diffusion_arc.py                      |    2 +-
 uv.lock                                     | 1131 +++++++++++++++++++
 12 files changed, 1977 insertions(+), 365 deletions(-)
 create mode 100644 .python-version
 delete mode 100644 batch_visualization.py
 delete mode 100644 dllm/visualize_sampling.py
 create mode 100644 pyproject.toml
 create mode 100644 sample_diffusion_arc.py
 rename {dllm => src/dllm}/__init__.py (86%)
 rename {dllm => src/dllm}/arc_dataset.py (100%)
 rename {dllm => src/dllm}/diffusion_transformer.py (80%)
 create mode 100644 src/dllm/visualize_sampling.py
 create mode 100644 tests/test_corruption_visualization.py
 create mode 100644 uv.lock

diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..e4fba21
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/batch_visualization.py b/batch_visualization.py
deleted file mode 100644
index cdf5a3b..0000000
--- a/batch_visualization.py
+++ /dev/null
@@ -1,155 +0,0 @@
-#!/usr/bin/env python3
-"""Generate batch visualizations for ARC train/validation splits."""
-
-from __future__ import annotations
-
-import argparse
-from pathlib import Path
-from typing import List
-
-import matplotlib.pyplot as plt
-import torch
-from torch.utils.data import DataLoader, random_split
-
-from dllm import (
-    ARCTaskDataset,
-    DiffusionTransformer,
-    DiffusionTransformerConfig,
-    arc_collate,
-    build_diffusion_schedule,
-    create_batch_visualization,
-)
-
-
-def parse_args(argv: List[str] | None = None) -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "data_dir",
-        type=str,
-        help="Path to the ARC dataset root containing the 'training' folder.",
-    )
-    parser.add_argument("--output-dir", type=str, default="outputs/visualizations")
-    parser.add_argument("--batch-size", type=int, default=8)
-    parser.add_argument("--num-batches", type=int, default=5)
-    parser.add_argument("--examples-per-batch", type=int, default=1)
-    parser.add_argument("--val-fraction", type=float, default=0.1)
-    parser.add_argument("--max-grid-size", type=int, default=30)
-    parser.add_argument("--timesteps", type=int, default=100)
-    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
-    parser.add_argument("--seed", type=int, default=42)
-    parser.add_argument("--d-model", type=int, default=288)
-    parser.add_argument("--num-heads", type=int, default=8)
-    parser.add_argument("--num-layers", type=int, default=7)
-    parser.add_argument("--dim-feedforward", type=int, default=1152)
-    parser.add_argument("--time-embed-dim", type=int, default=512)
-    parser.add_argument(
-        "--checkpoint",
-        type=str,
-        default="",
-        help="Optional path to a checkpoint produced by train_diffusion_arc.py.",
-    )
-    return parser.parse_args(argv)
-
-
-def set_seed(seed: int) -> None:
-    torch.manual_seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed_all(seed)
-
-
-def _gather_batches(loader: DataLoader, count: int) -> List[dict[str, torch.Tensor]]:
-    batches: List[dict[str, torch.Tensor]] = []
-    for batch in loader:
-        batches.append(batch)
-        if len(batches) >= count:
-            break
-    return batches
-
-
-def main(argv: List[str] | None = None) -> None:
-    args = parse_args(argv)
-    set_seed(args.seed)
-
-    device = torch.device(args.device)
-
-    dataset = ARCTaskDataset(
-        args.data_dir,
-        split="training",
-        max_grid_size=args.max_grid_size,
-    )
-    val_size = max(1, int(len(dataset) * args.val_fraction))
-    train_size = len(dataset) - val_size
-    generator = torch.Generator().manual_seed(args.seed)
-    train_dataset, val_dataset = random_split(
-        dataset,
-        [train_size, val_size],
-        generator=generator,
-    )
-
-    train_loader = DataLoader(
-        train_dataset,
-        batch_size=args.batch_size,
-        shuffle=True,
-        collate_fn=arc_collate,
-        num_workers=0,
-    )
-    val_loader = DataLoader(
-        val_dataset,
-        batch_size=args.batch_size,
-        shuffle=False,
-        collate_fn=arc_collate,
-        num_workers=0,
-    )
-
-    config = DiffusionTransformerConfig(
-        max_timesteps=args.timesteps,
-        max_grid_size=args.max_grid_size,
-        d_model=args.d_model,
-        num_heads=args.num_heads,
-        num_layers=args.num_layers,
-        dim_feedforward=args.dim_feedforward,
-        time_embed_dim=args.time_embed_dim,
-    )
-    model = DiffusionTransformer(config).to(device)
-    if args.checkpoint:
-        state = torch.load(args.checkpoint, map_location=device)
-        model.load_state_dict(state["model"])
-
-    schedule = build_diffusion_schedule(args.timesteps, device=device)
-
-    train_batches = _gather_batches(train_loader, args.num_batches)
-    val_batches = _gather_batches(val_loader, args.num_batches)
-
-    output_dir = Path(args.output_dir)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    train_fig = create_batch_visualization(
-        train_batches,
-        model=model,
-        diffusion_schedule=schedule,
-        max_grid_size=args.max_grid_size,
-        examples_per_batch=args.examples_per_batch,
-        title="Training batches",
-    )
-    train_path = output_dir / "train_batches.png"
-    train_fig.savefig(train_path, bbox_inches="tight")
-    plt.close(train_fig)
-
-    val_fig = create_batch_visualization(
-        val_batches,
-        model=model,
-        diffusion_schedule=schedule,
-        max_grid_size=args.max_grid_size,
-        examples_per_batch=args.examples_per_batch,
-        title="Validation batches",
-    )
-    val_path = output_dir / "val_batches.png"
-    val_fig.savefig(val_path, bbox_inches="tight")
-    plt.close(val_fig)
-
-    print(f"Saved training visualization to {train_path}")
-    print(f"Saved validation visualization to {val_path}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/dllm/visualize_sampling.py b/dllm/visualize_sampling.py
deleted file mode 100644
index 50281be..0000000
--- a/dllm/visualize_sampling.py
+++ /dev/null
@@ -1,203 +0,0 @@
-"""Utilities for visualizing ARC batches and diffusion corruption."""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Dict, List, Sequence
-
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-from .diffusion_transformer import DiffusionTransformer
-
-# ARC uses 10 colors (0-9). Index 10 is reserved for padding/empty cells.
-# The palette below mirrors the canonical ARC visualization colors.
-_COLOR_PALETTE = np.array(
-    [
-        (0, 0, 0),  # 0 - black
-        (0, 119, 187),  # 1 - blue
-        (221, 95, 0),  # 2 - orange
-        (0, 158, 115),  # 3 - green
-        (204, 204, 0),  # 4 - yellow
-        (148, 0, 211),  # 5 - purple
-        (255, 105, 180),  # 6 - pink
-        (0, 191, 196),  # 7 - cyan
-        (255, 0, 0),  # 8 - red
-        (128, 128, 128),  # 9 - gray
-        (255, 255, 255),  # 10 - white / padding
-    ],
-    dtype=np.float32,
-) / 255.0
-
-
-@dataclass
-class ExampleVisualization:
-    """Container for a single visualization example."""
-
-    condition: torch.Tensor
-    condition_mask: torch.Tensor
-    target: torch.Tensor
-    target_mask: torch.Tensor
-    corrupted: torch.Tensor
-    timestep: int
-    batch_index: int
-    example_index: int
-
-
-def decode_tokens(embeddings: torch.Tensor, token_embed: torch.nn.Embedding) -> torch.Tensor:
-    """Map embedding vectors back to discrete token ids via nearest embedding."""
-
-    weight = token_embed.weight
-    logits = torch.einsum("bld,vd->blv", embeddings, weight)
-    tokens = logits.argmax(dim=-1)
-    return tokens
-
-
-def _tokens_to_color_grid(
-    tokens: torch.Tensor,
-    mask: torch.Tensor,
-    *,
-    max_grid_size: int,
-    pad_token_id: int = 10,
-) -> np.ndarray:
-    """Convert a flattened token grid into an RGB image using the ARC palette."""
-
-    grid_tokens = tokens.view(max_grid_size, max_grid_size).cpu().numpy()
-    grid_mask = mask.view(max_grid_size, max_grid_size).cpu().numpy()
-    painted = np.full_like(grid_tokens, fill_value=pad_token_id)
-    painted[grid_mask > 0.5] = grid_tokens[grid_mask > 0.5]
-    return _COLOR_PALETTE[painted]
-
-
-def _plot_single(ax: plt.Axes, grid: np.ndarray, title: str) -> None:
-    ax.imshow(grid, interpolation="nearest")
-    ax.set_title(title)
-    ax.set_xticks([])
-    ax.set_yticks([])
-    ax.grid(False)
-
-
-def _collect_examples(
-    batches: Sequence[Dict[str, torch.Tensor]],
-    *,
-    model: DiffusionTransformer,
-    diffusion_schedule: Dict[str, torch.Tensor],
-    max_examples_per_batch: int,
-) -> List[ExampleVisualization]:
-    device = next(model.parameters()).device
-    examples: List[ExampleVisualization] = []
-
-    sqrt_alphas_cumprod = diffusion_schedule["sqrt_alphas_cumprod"]
-    sqrt_one_minus = diffusion_schedule["sqrt_one_minus"]
-
-    for batch_index, batch in enumerate(batches):
-        condition = batch["condition"].to(device)
-        condition_mask = batch["condition_mask"].to(device)
-        target = batch["target"].to(device)
-        target_mask = batch["target_mask"].to(device)
-
-        batch_size = target.size(0)
-        take = min(max_examples_per_batch, batch_size)
-        if take == 0:
-            continue
-
-        timesteps = torch.randint(
-            low=0,
-            high=model.config.max_timesteps,
-            size=(batch_size,),
-            device=device,
-        )
-        target_embed = model.token_embed(target) * target_mask.unsqueeze(-1)
-        noise = torch.randn_like(target_embed)
-        sqrt_alpha = sqrt_alphas_cumprod[timesteps].view(-1, 1, 1)
-        sqrt_one = sqrt_one_minus[timesteps].view(-1, 1, 1)
-        noisy = sqrt_alpha * target_embed + sqrt_one * noise
-        corrupted_tokens = decode_tokens(noisy, model.token_embed)
-
-        for example_index in range(take):
-            examples.append(
-                ExampleVisualization(
-                    condition=condition[example_index].cpu(),
-                    condition_mask=condition_mask[example_index].cpu(),
-                    target=target[example_index].cpu(),
-                    target_mask=target_mask[example_index].cpu(),
-                    corrupted=corrupted_tokens[example_index].cpu(),
-                    timestep=int(timesteps[example_index].item()),
-                    batch_index=batch_index,
-                    example_index=example_index,
-                )
-            )
-    return examples
-
-
-def create_batch_visualization(
-    batches: Sequence[Dict[str, torch.Tensor]],
-    *,
-    model: DiffusionTransformer,
-    diffusion_schedule: Dict[str, torch.Tensor],
-    max_grid_size: int,
-    examples_per_batch: int = 1,
-    title: str | None = None,
-) -> plt.Figure:
-    """Create a matplotlib figure visualizing dataset batches and corruption."""
-
-    examples = _collect_examples(
-        batches,
-        model=model,
-        diffusion_schedule=diffusion_schedule,
-        max_examples_per_batch=examples_per_batch,
-    )
-    if not examples:
-        raise ValueError("No examples available to visualize.")
-
-    n_rows = len(examples)
-    fig, axes = plt.subplots(n_rows, 3, figsize=(9, max(3, 3 * n_rows / 2)))
-    if title:
-        fig.suptitle(title, fontsize=16)
-
-    if n_rows == 1:
-        axes = np.expand_dims(axes, axis=0)
-
-    for row, example in enumerate(examples):
-        row_axes = axes[row]
-        cond_grid = _tokens_to_color_grid(
-            example.condition,
-            example.condition_mask,
-            max_grid_size=max_grid_size,
-        )
-        tgt_grid = _tokens_to_color_grid(
-            example.target,
-            example.target_mask,
-            max_grid_size=max_grid_size,
-        )
-        corrupted_grid = _tokens_to_color_grid(
-            example.corrupted,
-            example.target_mask,
-            max_grid_size=max_grid_size,
-        )
-
-        _plot_single(
-            row_axes[0],
-            cond_grid,
-            title=f"Batch {example.batch_index + 1} Cond #{example.example_index + 1}",
-        )
-        _plot_single(row_axes[1], tgt_grid, title="Target")
-        _plot_single(
-            row_axes[2],
-            corrupted_grid,
-            title=f"Corrupted (t={example.timestep})",
-        )
-
-    if title:
-        fig.tight_layout(rect=(0, 0, 1, 0.96))
-    else:
-        fig.tight_layout()
-    return fig
-
-
-__all__ = [
-    "ExampleVisualization",
-    "create_batch_visualization",
-    "decode_tokens",
-]
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..a12cf14
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,13 @@
+[project]
+name = "dllm"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "matplotlib>=3.10.7",
+    "numpy>=2.3.3",
+    "pytest>=8.4.2",
+    "torch>=2.8.0",
+    "transformers>=4.57.0",
+]
diff --git a/sample_diffusion_arc.py b/sample_diffusion_arc.py
new file mode 100644
index 0000000..e4963fc
--- /dev/null
+++ b/sample_diffusion_arc.py
@@ -0,0 +1,352 @@
+#!/usr/bin/env python3
+"""Sample from a pretrained diffusion transformer on ARC validation set."""
+
+from __future__ import annotations
+
+import argparse
+import os
+from pathlib import Path
+from typing import Dict, Optional
+
+import torch
+from torch.utils.data import DataLoader, random_split
+
+from dllm import (
+    ARCTaskDataset,
+    DiffusionTransformer,
+    DiffusionTransformerConfig,
+    arc_collate,
+    build_diffusion_schedule,
+)
+
+
+def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("checkpoint", type=str, help="Path to checkpoint file")
+    parser.add_argument("data_dir", type=str, help="Path to ARC dataset root directory")
+    parser.add_argument("--output-dir", type=str, default="outputs/samples")
+    parser.add_argument("--batch-size", type=int, default=16)
+    parser.add_argument("--timesteps", type=int, default=50)
+    parser.add_argument("--sampling-steps", type=int, default=None, help="Number of denoising steps (defaults to timesteps)")
+    parser.add_argument("--val-fraction", type=float, default=0.1)
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
+    parser.add_argument("--guidance-scale", type=float, default=1.0, help="Guidance scale for sampling")
+    parser.add_argument("--num-workers", type=int, default=2)
+    parser.add_argument("--max-batches", type=int, default=None, help="Max batches to sample (None = all)")
+    parser.add_argument("--max-grid-size", type=int, default=30)
+    parser.add_argument("--d-model", type=int, default=288)
+    parser.add_argument("--num-heads", type=int, default=8)
+    parser.add_argument("--num-layers", type=int, default=7)
+    parser.add_argument("--dim-feedforward", type=int, default=1152)
+    parser.add_argument("--time-embed-dim", type=int, default=512)
+    parser.add_argument("--use-ema", action="store_true", help="Use EMA weights if available")
+    parser.add_argument("--save-samples", action="store_true", help="Save generated samples to disk")
+    parser.add_argument("--visualize", action="store_true", help="Print text visualization of samples")
+    return parser.parse_args(argv)
+
+
+def set_seed(seed: int) -> None:
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+
+
+def to_device(batch: Dict[str, torch.Tensor], device: torch.device) -> Dict[str, torch.Tensor]:
+    return {k: v.to(device) for k, v in batch.items()}
+
+
+def decode_tokens(embeddings: torch.Tensor, token_embed: torch.nn.Embedding) -> torch.Tensor:
+    """Convert embeddings back to token IDs."""
+    weight = token_embed.weight
+    logits = torch.einsum("bld,vd->blv", embeddings, weight)
+    tokens = logits.argmax(dim=-1)
+    return tokens
+
+
+def compute_accuracy(
+    pred_tokens: torch.Tensor,
+    target_tokens: torch.Tensor,
+    target_mask: torch.Tensor,
+) -> float:
+    """Compute per-token accuracy on valid (non-padded) positions."""
+    matches = (pred_tokens == target_tokens).float() * target_mask
+    accuracy = matches.sum() / target_mask.sum().clamp(min=1.0)
+    return accuracy.item()
+
+
+def visualize_grids(
+    condition: torch.Tensor,
+    target: torch.Tensor,
+    prediction: torch.Tensor,
+    condition_mask: torch.Tensor,
+    target_mask: torch.Tensor,
+    grid_size: int = 30,
+    num_examples: int = 3,
+) -> None:
+    """Print colored block visualization of condition, target, and predicted grids."""
+    # ANSI color codes for ARC color palette
+    DEFAULT_ANSI = [
+        "38;5;254m",  # 0: white
+        "36m",  # 1: cyan
+        "31m",  # 2: red
+        "38;5;219m",  # 3: light pink
+        "33m",  # 4: yellow
+        "38;5;87m",  # 5: light turquoise
+        "32m",  # 6: green
+        "38;5;106m",  # 7: olive green
+        "34m",  # 8: blue
+        "38;5;208m",  # 9: orange
+        "35m",  # 10: magenta (pad token)
+    ]
+    BLOCK = "██"
+
+    def fmt_block(v: int) -> str:
+        code = DEFAULT_ANSI[min(int(v), 10)]
+        return f"\033[{code}{BLOCK}\033[0m"
+
+    batch_size = condition.size(0)
+    num_to_show = min(num_examples, batch_size)
+
+    for idx in range(num_to_show):
+        print(f"\n{'='*60}")
+        print(f"Example {idx + 1}:")
+        print(f"{'='*60}")
+
+        cond_grid = condition[idx].view(grid_size, grid_size).cpu().numpy()
+        targ_grid = target[idx].view(grid_size, grid_size).cpu().numpy()
+        pred_grid = prediction[idx].view(grid_size, grid_size).cpu().numpy()
+        cond_mask = condition_mask[idx].view(grid_size, grid_size).cpu().numpy()
+        targ_mask = target_mask[idx].view(grid_size, grid_size).cpu().numpy()
+
+        # Find actual grid boundaries
+        cond_h, cond_w = 0, 0
+        targ_h, targ_w = 0, 0
+        for i in range(grid_size):
+            if cond_mask[i, :].sum() > 0:
+                cond_h = i + 1
+            if targ_mask[i, :].sum() > 0:
+                targ_h = i + 1
+        for j in range(grid_size):
+            if cond_mask[:, j].sum() > 0:
+                cond_w = j + 1
+            if targ_mask[:, j].sum() > 0:
+                targ_w = j + 1
+
+        print("\nInput  →  Target  →  Prediction")
+        print("-" * 60)
+
+        # Print grids side by side
+        max_h = max(cond_h, targ_h)
+        for i in range(max_h):
+            # Input grid
+            if i < cond_h:
+                input_line = "".join(fmt_block(cond_grid[i, j]) for j in range(cond_w))
+            else:
+                input_line = ""
+
+            # Target grid
+            if i < targ_h:
+                target_line = "".join(fmt_block(targ_grid[i, j]) for j in range(targ_w))
+            else:
+                target_line = ""
+
+            # Prediction grid
+            if i < targ_h:
+                pred_line = "".join(fmt_block(pred_grid[i, j]) for j in range(targ_w))
+            else:
+                pred_line = ""
+
+            print(f"{input_line}  →  {target_line}  →  {pred_line}")
+
+
+@torch.no_grad()
+def sample_validation_set(
+    model: DiffusionTransformer,
+    loader: DataLoader,
+    schedule: Dict[str, torch.Tensor],
+    device: torch.device,
+    sampling_steps: Optional[int] = None,
+    guidance_scale: float = 1.0,
+    max_batches: Optional[int] = None,
+    save_dir: Optional[Path] = None,
+    visualize: bool = False,
+) -> Dict[str, float]:
+    """Sample from the model on validation set and compute metrics."""
+    model.eval()
+
+    total_accuracy = 0.0
+    total_samples = 0
+    batch_accuracies = []
+    all_samples = []
+
+    num_batches = max_batches or len(loader)
+
+    print(f"\nSampling from {num_batches} batches...")
+
+    for batch_idx, batch in enumerate(loader):
+        if max_batches is not None and batch_idx >= max_batches:
+            break
+
+        batch = to_device(batch, device)
+
+        # Sample from the diffusion model
+        sampled_embeddings = model.sample(
+            batch["condition"],
+            batch["condition_mask"],
+            schedule,
+            steps=sampling_steps,
+            guidance_scale=guidance_scale,
+        )
+
+        # Decode embeddings to tokens
+        pred_tokens = decode_tokens(sampled_embeddings, model.token_embed)
+        target_tokens = batch["target"]
+
+        # Compute accuracy
+        batch_acc = compute_accuracy(pred_tokens, target_tokens, batch["target_mask"])
+        batch_accuracies.append(batch_acc)
+
+        batch_samples = pred_tokens.size(0)
+        total_accuracy += batch_acc * batch_samples
+        total_samples += batch_samples
+
+        print(f"Batch {batch_idx + 1}/{num_batches}: accuracy={batch_acc:.4f}")
+
+        # Visualize first batch if requested
+        if visualize and batch_idx == 0:
+            visualize_grids(
+                batch["condition"],
+                target_tokens,
+                pred_tokens,
+                batch["condition_mask"],
+                batch["target_mask"],
+                grid_size=model.config.max_grid_size,
+                num_examples=3,
+            )
+
+        # Store samples if saving
+        if save_dir is not None:
+            all_samples.append({
+                "condition": batch["condition"].cpu(),
+                "condition_mask": batch["condition_mask"].cpu(),
+                "target": target_tokens.cpu(),
+                "target_mask": batch["target_mask"].cpu(),
+                "prediction": pred_tokens.cpu(),
+            })
+
+    # Save samples to disk
+    if save_dir is not None:
+        save_path = save_dir / "validation_samples.pt"
+        torch.save(all_samples, save_path)
+        print(f"\nSaved {len(all_samples)} batches to {save_path}")
+
+    # Compute overall metrics
+    avg_accuracy = total_accuracy / max(total_samples, 1)
+
+    return {
+        "accuracy": avg_accuracy,
+        "num_samples": total_samples,
+        "num_batches": len(batch_accuracies),
+    }
+
+
+def main(argv: list[str] | None = None) -> None:
+    args = parse_args(argv)
+    set_seed(args.seed)
+
+    device = torch.device(args.device)
+
+    # Create output directory if saving
+    save_dir = None
+    if args.save_samples:
+        save_dir = Path(args.output_dir)
+        os.makedirs(save_dir, exist_ok=True)
+
+    # Load dataset
+    print(f"Loading dataset from {args.data_dir}...")
+    dataset = ARCTaskDataset(
+        args.data_dir,
+        split="training",
+        max_grid_size=args.max_grid_size,
+        augment=False,  # No augmentation for validation
+    )
+
+    # Split into train/val (same as training script)
+    val_size = max(1, int(len(dataset) * args.val_fraction))
+    train_size = len(dataset) - val_size
+    generator = torch.Generator().manual_seed(args.seed)
+    _, val_dataset = random_split(
+        dataset, [train_size, val_size], generator=generator,
+    )
+
+    val_loader = DataLoader(
+        val_dataset,
+        batch_size=args.batch_size,
+        shuffle=False,
+        collate_fn=arc_collate,
+        num_workers=args.num_workers,
+        pin_memory=True,
+    )
+
+    print(f"Validation set size: {len(val_dataset)}")
+
+    # Create model
+    config = DiffusionTransformerConfig(
+        max_timesteps=args.timesteps,
+        max_grid_size=args.max_grid_size,
+        d_model=args.d_model,
+        num_heads=args.num_heads,
+        num_layers=args.num_layers,
+        dim_feedforward=args.dim_feedforward,
+        time_embed_dim=args.time_embed_dim,
+    )
+    model = DiffusionTransformer(config).to(device)
+
+    # Load checkpoint
+    print(f"Loading checkpoint from {args.checkpoint}...")
+    state = torch.load(args.checkpoint, map_location=device)
+
+    if args.use_ema and "ema" in state:
+        print("Using EMA weights")
+        model.load_state_dict(state["ema"])
+    elif "model" in state:
+        model.load_state_dict(state["model"])
+    else:
+        # Assume raw state dict
+        model.load_state_dict(state)
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Model parameters: {total_params/1e6:.2f}M")
+
+    # Build diffusion schedule
+    schedule = build_diffusion_schedule(args.timesteps, device=device)
+
+    sampling_steps = args.sampling_steps or args.timesteps
+    print(f"Sampling with {sampling_steps} denoising steps (guidance scale: {args.guidance_scale})")
+
+    # Sample from validation set
+    metrics = sample_validation_set(
+        model,
+        val_loader,
+        schedule,
+        device,
+        sampling_steps=sampling_steps,
+        guidance_scale=args.guidance_scale,
+        max_batches=args.max_batches,
+        save_dir=save_dir,
+        visualize=args.visualize,
+    )
+
+    # Print results
+    print(f"\n{'='*60}")
+    print("Validation Results:")
+    print(f"{'='*60}")
+    print(f"Token Accuracy: {metrics['accuracy']:.4f}")
+    print(f"Total Samples:  {metrics['num_samples']}")
+    print(f"Batches:        {metrics['num_batches']}")
+    print(f"{'='*60}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/dllm/__init__.py b/src/dllm/__init__.py
similarity index 86%
rename from dllm/__init__.py
rename to src/dllm/__init__.py
index 2c95a5d..efe3152 100644
--- a/dllm/__init__.py
+++ b/src/dllm/__init__.py
@@ -11,7 +11,7 @@
 from .visualize_sampling import (
     ExampleVisualization,
     create_batch_visualization,
-    decode_tokens,
+    create_corruption_progression_visualization,
 )
 
 __all__ = [
@@ -24,5 +24,5 @@
     "timestep_embedding",
     "ExampleVisualization",
     "create_batch_visualization",
-    "decode_tokens",
+    "create_corruption_progression_visualization",
 ]
diff --git a/dllm/arc_dataset.py b/src/dllm/arc_dataset.py
similarity index 100%
rename from dllm/arc_dataset.py
rename to src/dllm/arc_dataset.py
diff --git a/dllm/diffusion_transformer.py b/src/dllm/diffusion_transformer.py
similarity index 80%
rename from dllm/diffusion_transformer.py
rename to src/dllm/diffusion_transformer.py
index 91719ee..73c46ae 100644
--- a/dllm/diffusion_transformer.py
+++ b/src/dllm/diffusion_transformer.py
@@ -10,9 +10,24 @@
 import torch.nn as nn
 
 
+def linear_beta_schedule(timesteps: int, beta_start: float = 1e-4, beta_end: float = 0.02) -> torch.Tensor:
+    """Linear schedule for beta values.
+
+    Args:
+        timesteps: Number of diffusion steps
+        beta_start: Starting beta value (low noise)
+        beta_end: Ending beta value (high noise)
+    """
+    return torch.linspace(beta_start, beta_end, timesteps)
+
+
 def cosine_beta_schedule(timesteps: int, s: float = 0.008) -> torch.Tensor:
-    """Cosine schedule from https://arxiv.org/abs/2102.09672."""
+    """Cosine schedule from https://arxiv.org/abs/2102.09672.
 
+    Args:
+        timesteps: Number of diffusion steps
+        s: Offset parameter controlling schedule steepness (higher = gentler)
+    """
     steps = timesteps + 1
     x = torch.linspace(0, timesteps, steps)
     alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * math.pi / 2) ** 2
@@ -31,7 +46,7 @@ class DiffusionTransformerConfig:
     num_layers: int = 7
     dim_feedforward: int = 1152
     dropout: float = 0.1
-    max_timesteps: int = 100
+    max_timesteps: int = 10
     time_embed_dim: int = 512
 
     @property
@@ -164,8 +179,34 @@ def timestep_embedding(timesteps: torch.Tensor, dim: int, max_period: int = 1000
     return embedding
 
 
-def build_diffusion_schedule(timesteps: int, device: torch.device) -> Dict[str, torch.Tensor]:
-    betas = cosine_beta_schedule(timesteps).to(device)
+def build_diffusion_schedule(
+    timesteps: int,
+    device: torch.device,
+    schedule_type: str = "cosine",
+    s: float = 0.008,
+    beta_start: float = 1e-4,
+    beta_end: float = 0.02,
+) -> Dict[str, torch.Tensor]:
+    """Build diffusion noise schedule.
+
+    Args:
+        timesteps: Number of diffusion steps
+        device: Device to place tensors on
+        schedule_type: Type of schedule ("linear" or "cosine")
+        s: Cosine schedule offset parameter (only used if schedule_type="cosine")
+        beta_start: Linear schedule start value (only used if schedule_type="linear")
+        beta_end: Linear schedule end value (only used if schedule_type="linear")
+
+    Returns:
+        Dictionary containing schedule tensors
+    """
+    if schedule_type == "linear":
+        betas = linear_beta_schedule(timesteps, beta_start=beta_start, beta_end=beta_end).to(device)
+    elif schedule_type == "cosine":
+        betas = cosine_beta_schedule(timesteps, s=s).to(device)
+    else:
+        raise ValueError(f"Unknown schedule_type: {schedule_type}. Must be 'linear' or 'cosine'")
+
     alphas = 1.0 - betas
     alphas_cumprod = torch.cumprod(alphas, dim=0)
     alphas_cumprod_prev = torch.cat([torch.ones(1, device=device), alphas_cumprod[:-1]], dim=0)
diff --git a/src/dllm/visualize_sampling.py b/src/dllm/visualize_sampling.py
new file mode 100644
index 0000000..61100b4
--- /dev/null
+++ b/src/dllm/visualize_sampling.py
@@ -0,0 +1,369 @@
+"""Utilities for visualizing ARC batches and diffusion corruption."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Dict, List, Sequence
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+
+# ARC uses 10 colors (0-9). Index 10 is reserved for padding/empty cells.
+# The palette below mirrors the canonical ARC visualization colors.
+_COLOR_PALETTE = (
+    np.array(
+        [
+            (0, 0, 0),  # 0 - black
+            (0, 119, 187),  # 1 - blue
+            (221, 95, 0),  # 2 - orange
+            (0, 158, 115),  # 3 - green
+            (204, 204, 0),  # 4 - yellow
+            (148, 0, 211),  # 5 - purple
+            (255, 105, 180),  # 6 - pink
+            (0, 191, 196),  # 7 - cyan
+            (255, 0, 0),  # 8 - red
+            (128, 128, 128),  # 9 - gray
+            (255, 255, 255),  # 10 - white / padding
+        ],
+        dtype=np.float32,
+    )
+    / 255.0
+)
+
+
+@dataclass
+class ExampleVisualization:
+    """Container for a single visualization example."""
+
+    condition: torch.Tensor
+    condition_mask: torch.Tensor
+    target: torch.Tensor
+    target_mask: torch.Tensor
+    corrupted: torch.Tensor
+    timestep: int
+    batch_index: int
+    example_index: int
+
+
+def _tokens_to_color_grid(
+    tokens: torch.Tensor,
+    mask: torch.Tensor,
+    *,
+    max_grid_size: int,
+    pad_token_id: int = 10,
+) -> np.ndarray:
+    """Convert a flattened token grid into an RGB image using the ARC palette."""
+
+    grid_tokens = tokens.view(max_grid_size, max_grid_size).cpu().numpy()
+    grid_mask = mask.view(max_grid_size, max_grid_size).cpu().numpy()
+    painted = np.full_like(grid_tokens, fill_value=pad_token_id)
+    painted[grid_mask > 0.5] = grid_tokens[grid_mask > 0.5]
+    return _COLOR_PALETTE[painted]
+
+
+def _plot_single(ax: plt.Axes, grid: np.ndarray, title: str) -> None:
+    ax.imshow(grid, interpolation="nearest")
+    ax.set_title(title)
+    ax.set_xticks([])
+    ax.set_yticks([])
+    ax.grid(False)
+
+
+def _corrupt_tokens(
+    tokens: torch.Tensor,
+    mask: torch.Tensor,
+    timesteps: torch.Tensor,
+    sqrt_alphas_cumprod: torch.Tensor,
+    sqrt_one_minus: torch.Tensor,
+    vocab_size: int = 11,
+) -> torch.Tensor:
+    """Add noise to tokens in probability space and return argmax tokens.
+
+    Args:
+        tokens: Target tokens [batch_size, seq_len]
+        mask: Token mask [batch_size, seq_len]
+        timesteps: Diffusion timestep for each sample [batch_size]
+        sqrt_alphas_cumprod: sqrt(alpha_bar_t) schedule
+        sqrt_one_minus: sqrt(1 - alpha_bar_t) schedule
+        vocab_size: Number of tokens in vocabulary
+
+    Returns:
+        Corrupted tokens [batch_size, seq_len] obtained by argmax over noisy logits
+    """
+    batch_size, seq_len = tokens.shape
+    device = tokens.device
+
+    # Create one-hot encodings of original tokens [batch_size, seq_len, vocab_size]
+    one_hot = torch.nn.functional.one_hot(tokens, num_classes=vocab_size).float()
+
+    # Add Gaussian noise to the one-hot probabilities
+    noise = torch.randn_like(one_hot)
+
+    # Get noise schedule values for each sample
+    sqrt_alpha = sqrt_alphas_cumprod[timesteps].view(-1, 1, 1)
+    sqrt_one = sqrt_one_minus[timesteps].view(-1, 1, 1)
+
+    # Apply noise: noisy_probs = sqrt_alpha * one_hot + sqrt_one * noise
+    noisy_probs = sqrt_alpha * one_hot + sqrt_one * noise
+
+    # Get discrete tokens via argmax
+    corrupted = noisy_probs.argmax(dim=-1)
+
+    # Apply mask to keep only valid positions
+    corrupted = corrupted * mask.long() + tokens * (1 - mask.long())
+
+    return corrupted
+
+
+def _collect_examples(
+    batches: Sequence[Dict[str, torch.Tensor]],
+    *,
+    diffusion_schedule: Dict[str, torch.Tensor],
+    max_examples_per_batch: int,
+    max_timesteps: int,
+    vocab_size: int = 11,
+    device: torch.device,
+) -> List[ExampleVisualization]:
+    """Collect visualization examples from batches by corrupting tokens."""
+    examples: List[ExampleVisualization] = []
+
+    sqrt_alphas_cumprod = diffusion_schedule["sqrt_alphas_cumprod"]
+    sqrt_one_minus = diffusion_schedule["sqrt_one_minus"]
+
+    for batch_index, batch in enumerate(batches):
+        condition = batch["condition"].to(device)
+        condition_mask = batch["condition_mask"].to(device)
+        target = batch["target"].to(device)
+        target_mask = batch["target_mask"].to(device)
+
+        batch_size = target.size(0)
+        take = min(max_examples_per_batch, batch_size)
+        if take == 0:
+            continue
+
+        # Sample random timesteps for each example
+        timesteps = torch.randint(
+            low=0,
+            high=max_timesteps,
+            size=(batch_size,),
+            device=device,
+        )
+
+        # Corrupt tokens by adding noise in probability space
+        corrupted_tokens = _corrupt_tokens(
+            target,
+            target_mask,
+            timesteps,
+            sqrt_alphas_cumprod,
+            sqrt_one_minus,
+            vocab_size=vocab_size,
+        )
+
+        for example_index in range(take):
+            examples.append(
+                ExampleVisualization(
+                    condition=condition[example_index].cpu(),
+                    condition_mask=condition_mask[example_index].cpu(),
+                    target=target[example_index].cpu(),
+                    target_mask=target_mask[example_index].cpu(),
+                    corrupted=corrupted_tokens[example_index].cpu(),
+                    timestep=int(timesteps[example_index].item()),
+                    batch_index=batch_index,
+                    example_index=example_index,
+                )
+            )
+    return examples
+
+
+def create_batch_visualization(
+    batches: Sequence[Dict[str, torch.Tensor]],
+    *,
+    diffusion_schedule: Dict[str, torch.Tensor],
+    max_grid_size: int,
+    max_timesteps: int = 50,
+    vocab_size: int = 11,
+    examples_per_batch: int = 1,
+    device: str | torch.device = "cpu",
+    title: str | None = None,
+) -> plt.Figure:
+    """Create a matplotlib figure visualizing dataset batches and corruption.
+
+    Args:
+        batches: Sequence of data batches containing condition and target tokens
+        diffusion_schedule: Dictionary with noise schedule tensors
+        max_grid_size: Maximum grid size for visualization
+        max_timesteps: Maximum number of diffusion timesteps
+        vocab_size: Size of token vocabulary
+        examples_per_batch: Number of examples to visualize per batch
+        device: Device to run corruption on
+        title: Optional title for the figure
+
+    Returns:
+        Matplotlib figure with visualizations
+    """
+    if isinstance(device, str):
+        device = torch.device(device)
+
+    examples = _collect_examples(
+        batches,
+        diffusion_schedule=diffusion_schedule,
+        max_examples_per_batch=examples_per_batch,
+        max_timesteps=max_timesteps,
+        vocab_size=vocab_size,
+        device=device,
+    )
+    if not examples:
+        raise ValueError("No examples available to visualize.")
+
+    n_rows = len(examples)
+    fig, axes = plt.subplots(n_rows, 3, figsize=(9, max(3, 3 * n_rows / 2)))
+    if title:
+        fig.suptitle(title, fontsize=16)
+
+    if n_rows == 1:
+        axes = np.expand_dims(axes, axis=0)
+
+    for row, example in enumerate(examples):
+        row_axes = axes[row]
+        cond_grid = _tokens_to_color_grid(
+            example.condition,
+            example.condition_mask,
+            max_grid_size=max_grid_size,
+        )
+        tgt_grid = _tokens_to_color_grid(
+            example.target,
+            example.target_mask,
+            max_grid_size=max_grid_size,
+        )
+        corrupted_grid = _tokens_to_color_grid(
+            example.corrupted,
+            example.target_mask,
+            max_grid_size=max_grid_size,
+        )
+
+        _plot_single(
+            row_axes[0],
+            cond_grid,
+            title=f"Batch {example.batch_index + 1} Cond #{example.example_index + 1}",
+        )
+        _plot_single(row_axes[1], tgt_grid, title="Target")
+        _plot_single(
+            row_axes[2],
+            corrupted_grid,
+            title=f"Corrupted (t={example.timestep})",
+        )
+
+    if title:
+        fig.tight_layout(rect=(0, 0, 1, 0.96))
+    else:
+        fig.tight_layout()
+    return fig
+
+
+def create_corruption_progression_visualization(
+    batch: Dict[str, torch.Tensor],
+    *,
+    diffusion_schedule: Dict[str, torch.Tensor],
+    max_grid_size: int,
+    max_timesteps: int = 50,
+    vocab_size: int = 11,
+    example_index: int = 0,
+    device: str | torch.device = "cpu",
+    title: str | None = None,
+) -> plt.Figure:
+    """Visualize corruption progression of a single example through all timesteps.
+
+    Args:
+        batch: A single batch containing condition and target tokens
+        diffusion_schedule: Dictionary with noise schedule tensors
+        max_grid_size: Maximum grid size for visualization
+        max_timesteps: Maximum number of diffusion timesteps
+        vocab_size: Size of token vocabulary
+        example_index: Which example from the batch to visualize
+        device: Device to run corruption on
+        title: Optional title for the figure
+
+    Returns:
+        Matplotlib figure showing the example at t=0, 1, 2, ..., max_timesteps
+    """
+    if isinstance(device, str):
+        device = torch.device(device)
+
+    sqrt_alphas_cumprod = diffusion_schedule["sqrt_alphas_cumprod"]
+    sqrt_one_minus = diffusion_schedule["sqrt_one_minus"]
+
+    condition = batch["condition"].to(device)
+    condition_mask = batch["condition_mask"].to(device)
+    target = batch["target"].to(device)
+    target_mask = batch["target_mask"].to(device)
+
+    # Take the specified example from the batch
+    condition = condition[example_index : example_index + 1]
+    condition_mask = condition_mask[example_index : example_index + 1]
+    target = target[example_index : example_index + 1]
+    target_mask = target_mask[example_index : example_index + 1]
+
+    # Generate corrupted versions for each timestep (0 to max_timesteps-1)
+    num_timesteps = max_timesteps
+    corrupted_at_each_t = []
+
+    for t in range(num_timesteps):
+        timesteps = torch.tensor([t], device=device)
+        corrupted = _corrupt_tokens(
+            target,
+            target_mask,
+            timesteps,
+            sqrt_alphas_cumprod,
+            sqrt_one_minus,
+            vocab_size=vocab_size,
+        )
+        corrupted_at_each_t.append(corrupted[0].cpu())
+
+    # Create visualization grid: 3 rows x (num_timesteps) columns
+    # Row 1: Condition (repeated)
+    # Row 2: Target (repeated)
+    # Row 3: Corrupted at each timestep
+    fig, axes = plt.subplots(3, num_timesteps, figsize=(2.5 * num_timesteps, 7.5))
+    if title:
+        fig.suptitle(title, fontsize=16, y=0.98)
+
+    for col in range(num_timesteps):
+        # Plot condition (same for all timesteps)
+        cond_grid = _tokens_to_color_grid(
+            condition[0].cpu(),
+            condition_mask[0].cpu(),
+            max_grid_size=max_grid_size,
+        )
+        _plot_single(axes[0, col], cond_grid, title=f"Condition" if col == 0 else "")
+
+        # Plot target (same for all timesteps)
+        tgt_grid = _tokens_to_color_grid(
+            target[0].cpu(),
+            target_mask[0].cpu(),
+            max_grid_size=max_grid_size,
+        )
+        _plot_single(
+            axes[1, col],
+            tgt_grid,
+            title=f"Target (t={col})" if col == 0 else f"t={col}",
+        )
+
+        # Plot corrupted at this timestep
+        corrupted_grid = _tokens_to_color_grid(
+            corrupted_at_each_t[col],
+            target_mask[0].cpu(),
+            max_grid_size=max_grid_size,
+        )
+        _plot_single(axes[2, col], corrupted_grid, title=f"Corrupted (t={col})")
+
+    fig.tight_layout()
+    return fig
+
+
+__all__ = [
+    "ExampleVisualization",
+    "create_batch_visualization",
+    "create_corruption_progression_visualization",
+]
diff --git a/tests/test_corruption_visualization.py b/tests/test_corruption_visualization.py
new file mode 100644
index 0000000..1c3c3e5
--- /dev/null
+++ b/tests/test_corruption_visualization.py
@@ -0,0 +1,63 @@
+"""Test corruption progression visualization."""
+
+from pathlib import Path
+
+import pytest
+import torch
+from torch.utils.data import DataLoader
+
+from dllm import (
+    ARCTaskDataset,
+    arc_collate,
+    build_diffusion_schedule,
+    create_corruption_progression_visualization,
+)
+
+
+@pytest.fixture
+def arc_batch():
+    """Load a single batch from the ARC dataset."""
+    data_dir = Path(__file__).parent.parent / "data" / "ARC-AGI-master" / "data"
+    if not data_dir.exists():
+        pytest.skip(f"ARC dataset not found at {data_dir}")
+
+    dataset = ARCTaskDataset(str(data_dir), split="training", max_grid_size=30)
+    loader = DataLoader(dataset, batch_size=8, shuffle=False, collate_fn=arc_collate)
+    return next(iter(loader))
+
+
+def test_corruption_progression_visualization(arc_batch):
+    """Test that corruption progression visualization can be created and saved."""
+    device = torch.device("cpu")
+    timesteps = 50
+
+    # Build diffusion schedule with linear progression for even corruption
+    # Linear schedule gives constant noise addition rate (more predictable/gradual)
+    # Adjust beta_end to control max corruption (0.02 is standard, lower = gentler)
+    schedule = build_diffusion_schedule(
+        timesteps, device=device, schedule_type="linear", beta_start=1e-4, beta_end=0.03
+    )
+
+    # Create visualization
+    fig = create_corruption_progression_visualization(
+        arc_batch,
+        diffusion_schedule=schedule,
+        max_grid_size=30,
+        max_timesteps=timesteps,
+        vocab_size=11,
+        example_index=0,
+        device=device,
+        title="Corruption Progression Test",
+    )
+
+    # Save output
+    output_dir = Path("outputs/test_visualizations")
+    output_dir.mkdir(parents=True, exist_ok=True)
+    output_path = output_dir / "test_corruption_progression.png"
+
+    fig.savefig(output_path, bbox_inches="tight", dpi=150)
+    print(f"Saved corruption progression visualization to {output_path}")
+
+    # Verify file was created
+    assert output_path.exists()
+    assert output_path.stat().st_size > 0
diff --git a/train_diffusion_arc.py b/train_diffusion_arc.py
index b9aa95d..6face90 100644
--- a/train_diffusion_arc.py
+++ b/train_diffusion_arc.py
@@ -36,7 +36,7 @@ def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
     parser.add_argument("--epochs", type=int, default=50)
     parser.add_argument("--lr", type=float, default=3e-4)
     parser.add_argument("--weight-decay", type=float, default=0.01)
-    parser.add_argument("--timesteps", type=int, default=100)
+    parser.add_argument("--timesteps", type=int, default=50)
     parser.add_argument("--val-fraction", type=float, default=0.1)
     parser.add_argument("--seed", type=int, default=42)
     parser.add_argument("--grad-clip", type=float, default=1.0)
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new file mode 100644
index 0000000..8812d3e
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1131 @@
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From 49d5a98266013b5c1b9970f6d28d70cb495b8a27 Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 14:19:55 +0100
Subject: [PATCH 10/13] yReplace CLI args with YAML config

---
 README.md                         |  70 ++++++++++-
 tests/test_train_diffusion_arc.py |  56 ++++-----
 train_diffusion_arc.py            | 198 ++++++++++++++++--------------
 3 files changed, 199 insertions(+), 125 deletions(-)

diff --git a/README.md b/README.md
index d045eab..4c0c1b7 100644
--- a/README.md
+++ b/README.md
@@ -13,9 +13,6 @@ cd data
 curl -L https://github.com/fchollet/ARC/archive/refs/heads/master.zip -o arc.zip
 unzip arc.zip 'ARC-AGI-master/data/*'
 cd ..
-
-# Run training
-python train_diffusion_arc.py data/ARC-AGI-master/data
 ```
 
 Any mirror with the same folder structure will also work. The `ARCTaskDataset` loader simply walks every `*.json` file inside the specified split directory.
@@ -30,6 +27,73 @@ python batch_visualization.py data/ARC-AGI-master/data --checkpoint outputs/diff
 
 This command saves `train_batches.png` and `val_batches.png` under `outputs/visualizations/`, each showing five batches of samples with the condition, target, and a randomly corrupted view at different diffusion timesteps (defaulting to a compact 0–99 range).
 
+## Configuration
+
+Training is configured through a YAML file validated by `DiffusionArcTrainingConfig` from `train_diffusion_arc.py` using [`pydantic_config`](https://github.com/samsja/pydantic_config). Install the dependency with:
+
+```bash
+pip install pydantic_config pyyaml
+```
+
+Create a YAML file describing your run. Every field has a sensible default except `data_dir` which must point at the ARC dataset root. The available options are:
+
+| Field | Description |
+| --- | --- |
+| `data_dir` | Path to the ARC dataset root containing `training/` and `evaluation/` folders. |
+| `output_dir` | Directory where checkpoints and the final model will be written. |
+| `batch_size` | Batch size for both training and validation loaders. |
+| `epochs` | Number of full passes over the training set. |
+| `lr` / `weight_decay` | AdamW optimizer hyper-parameters. |
+| `timesteps` | Number of diffusion steps in the schedule. |
+| `val_fraction` | Fraction of the dataset used for validation. |
+| `seed` | Random seed for Python, PyTorch and data splits. |
+| `grad_clip` | Gradient clipping value (set to `0` to disable). |
+| `device` | Device string understood by `torch.device`, defaults to `cuda` when available. |
+| `ema` | Exponential moving average decay for model weights (disabled when `0`). |
+| `duality_weight` | Weight applied to the clean target reconstruction loss term. |
+| `log_interval` | Number of training steps between log messages. |
+| `num_workers` | Data loader worker count. |
+| `save_interval` | Save a checkpoint every N epochs. |
+| `resume` | Optional path to a checkpoint to resume from. |
+| `augment` | Enable random grid flips during dataset loading. |
+| `mixed_precision` | Enable automatic mixed precision training. |
+| `max_grid_size`, `d_model`, `num_heads`, `num_layers`, `dim_feedforward`, `time_embed_dim` | Architectural parameters passed to `DiffusionTransformerConfig`. |
+
+Example configuration:
+
+```yaml
+data_dir: data/ARC-master/data
+output_dir: outputs/diffusion_arc
+batch_size: 32
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+timesteps: 1000
+val_fraction: 0.1
+seed: 42
+grad_clip: 1.0
+device: cuda
+ema: 0.0
+duality_weight: 0.5
+log_interval: 100
+num_workers: 2
+save_interval: 5
+augment: false
+mixed_precision: false
+max_grid_size: 30
+d_model: 288
+num_heads: 8
+num_layers: 7
+dim_feedforward: 1152
+time_embed_dim: 512
+```
+
+Run training by pointing the script at your YAML file:
+
+```bash
+python train_diffusion_arc.py path/to/config.yaml
+```
+
 ## Tests
 
 A minimal CPU smoke test is available via:
diff --git a/tests/test_train_diffusion_arc.py b/tests/test_train_diffusion_arc.py
index b29afed..a82fdc8 100644
--- a/tests/test_train_diffusion_arc.py
+++ b/tests/test_train_diffusion_arc.py
@@ -1,5 +1,6 @@
 import json
 import sys
+import textwrap
 from pathlib import Path
 
 import pytest
@@ -35,37 +36,30 @@ def test_main_runs_with_tiny_model(tmp_path: Path) -> None:
 
     output_dir = tmp_path / "outputs"
 
-    argv = [
-        str(data_dir),
-        "--output-dir",
-        str(output_dir),
-        "--batch-size",
-        "1",
-        "--epochs",
-        "1",
-        "--timesteps",
-        "4",
-        "--num-workers",
-        "0",
-        "--log-interval",
-        "1",
-        "--skip-param-check",
-        "--device",
-        "cpu",
-        "--max-grid-size",
-        "3",
-        "--d-model",
-        "16",
-        "--num-heads",
-        "4",
-        "--num-layers",
-        "1",
-        "--dim-feedforward",
-        "32",
-        "--time-embed-dim",
-        "32",
-    ]
+    config_path = tmp_path / "config.yaml"
+    config_path.write_text(
+        textwrap.dedent(
+            f"""
+            data_dir: {data_dir}
+            output_dir: {output_dir}
+            batch_size: 1
+            epochs: 1
+            timesteps: 4
+            num_workers: 0
+            log_interval: 1
+            device: cpu
+            max_grid_size: 3
+            d_model: 16
+            num_heads: 4
+            num_layers: 1
+            dim_feedforward: 32
+            time_embed_dim: 32
+            duality_weight: 0.0
+            """
+        ).strip()
+        + "\n"
+    )
 
-    main(argv)
+    main(config_path)
 
     assert (output_dir / "final_model.pt").exists()
diff --git a/train_diffusion_arc.py b/train_diffusion_arc.py
index 6face90..9c40699 100644
--- a/train_diffusion_arc.py
+++ b/train_diffusion_arc.py
@@ -3,14 +3,18 @@
 
 from __future__ import annotations
 
-import argparse
 import os
+import sys
 from pathlib import Path
-from typing import Dict
+from typing import Dict, Sequence
 
 import torch
 from torch.utils.data import DataLoader, random_split
 
+from pydantic import Field
+from pydantic_config import SettingsConfig, SettingsModel
+from pydantic_config.main import ConfigFileSettingsSource
+
 from dllm import (
     ARCTaskDataset,
     DiffusionTransformer,
@@ -19,48 +23,50 @@
     build_diffusion_schedule,
 )
 
-
-def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "data_dir",
-        type=str,
-        help=(
-            "Path to ARC dataset root directory (the folder containing the "
-            "'training' and 'evaluation' sub-directories from the official "
-            "fchollet/ARC data dump)."
-        ),
-    )
-    parser.add_argument("--output-dir", type=str, default="outputs/diffusion_arc")
-    parser.add_argument("--batch-size", type=int, default=32)
-    parser.add_argument("--epochs", type=int, default=50)
-    parser.add_argument("--lr", type=float, default=3e-4)
-    parser.add_argument("--weight-decay", type=float, default=0.01)
-    parser.add_argument("--timesteps", type=int, default=50)
-    parser.add_argument("--val-fraction", type=float, default=0.1)
-    parser.add_argument("--seed", type=int, default=42)
-    parser.add_argument("--grad-clip", type=float, default=1.0)
-    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
-    parser.add_argument("--ema", type=float, default=0.0, help="EMA decay for weights")
-    parser.add_argument("--duality-weight", type=float, default=0.5)
-    parser.add_argument("--log-interval", type=int, default=100)
-    parser.add_argument("--num-workers", type=int, default=2)
-    parser.add_argument("--save-interval", type=int, default=5)
-    parser.add_argument("--resume", type=str, default="", help="Resume checkpoint path")
-    parser.add_argument("--augment", action="store_true", help="Enable random flips for augmentation")
-    parser.add_argument("--mixed-precision", action="store_true")
-    parser.add_argument("--max-grid-size", type=int, default=30)
-    parser.add_argument("--d-model", type=int, default=288)
-    parser.add_argument("--num-heads", type=int, default=8)
-    parser.add_argument("--num-layers", type=int, default=7)
-    parser.add_argument("--dim-feedforward", type=int, default=1152)
-    parser.add_argument("--time-embed-dim", type=int, default=512)
-    parser.add_argument(
-        "--skip-param-check",
-        action="store_true",
-        help="Skip enforcing the ~7M parameter count. Useful for tests.",
-    )
-    return parser.parse_args(argv)
+def _default_device() -> str:
+    return "cuda" if torch.cuda.is_available() else "cpu"
+
+
+class DiffusionArcTrainingConfig(SettingsModel):
+    """Configuration for diffusion transformer ARC training."""
+
+    data_dir: Path
+    output_dir: Path = Path("outputs/diffusion_arc")
+    batch_size: int = 32
+    epochs: int = 50
+    lr: float = 3e-4
+    weight_decay: float = 0.01
+    timesteps: int = 1000
+    val_fraction: float = 0.1
+    seed: int = 42
+    grad_clip: float = 1.0
+    device: str = Field(default_factory=_default_device)
+    ema: float = 0.0
+    duality_weight: float = 0.5
+    log_interval: int = 100
+    num_workers: int = 2
+    save_interval: int = 5
+    resume: Path | None = None
+    augment: bool = False
+    mixed_precision: bool = False
+    max_grid_size: int = 30
+    d_model: int = 288
+    num_heads: int = 8
+    num_layers: int = 7
+    dim_feedforward: int = 1152
+    time_embed_dim: int = 512
+
+    model_config = SettingsConfig(extra="forbid")
+
+    @classmethod
+    def from_yaml(cls, path: Path | str) -> "DiffusionArcTrainingConfig":
+        source = ConfigFileSettingsSource(
+            cls,
+            config_file=Path(path),
+            config_file_required=True,
+        )
+        data = source()
+        return cls.model_validate(data)
 
 
 def set_seed(seed: int) -> None:
@@ -79,107 +85,111 @@ def to_device(batch: Dict[str, torch.Tensor], device: torch.device) -> Dict[str,
     return {k: v.to(device) for k, v in batch.items()}
 
 
-def main(argv: list[str] | None = None) -> None:
-    args = parse_args(argv)
-    set_seed(args.seed)
+def load_training_config(config: DiffusionArcTrainingConfig | Path | str) -> DiffusionArcTrainingConfig:
+    if isinstance(config, DiffusionArcTrainingConfig):
+        return config
+    return DiffusionArcTrainingConfig.from_yaml(config)
 
-    device = torch.device(args.device)
-    os.makedirs(args.output_dir, exist_ok=True)
+
+def main(config: DiffusionArcTrainingConfig | Path | str) -> None:
+    cfg = load_training_config(config)
+    set_seed(cfg.seed)
+
+    device = torch.device(cfg.device)
+    os.makedirs(cfg.output_dir, exist_ok=True)
 
     dataset = ARCTaskDataset(
-        args.data_dir,
+        cfg.data_dir,
         split="training",
-        max_grid_size=args.max_grid_size,
-        augment=args.augment,
+        max_grid_size=cfg.max_grid_size,
+        augment=cfg.augment,
     )
-    val_size = max(1, int(len(dataset) * args.val_fraction))
+    val_size = max(1, int(len(dataset) * cfg.val_fraction))
     train_size = len(dataset) - val_size
-    generator = torch.Generator().manual_seed(args.seed)
+    generator = torch.Generator().manual_seed(cfg.seed)
     train_dataset, val_dataset = random_split(
         dataset, [train_size, val_size], generator=generator,
     )
 
     train_loader = DataLoader(
         train_dataset,
-        batch_size=args.batch_size,
+        batch_size=cfg.batch_size,
         shuffle=True,
         collate_fn=arc_collate,
-        num_workers=args.num_workers,
+        num_workers=cfg.num_workers,
         pin_memory=True,
     )
     val_loader = DataLoader(
         val_dataset,
-        batch_size=args.batch_size,
+        batch_size=cfg.batch_size,
         shuffle=False,
         collate_fn=arc_collate,
-        num_workers=args.num_workers,
+        num_workers=cfg.num_workers,
         pin_memory=True,
     )
 
-    config = DiffusionTransformerConfig(
-        max_timesteps=args.timesteps,
-        max_grid_size=args.max_grid_size,
-        d_model=args.d_model,
-        num_heads=args.num_heads,
-        num_layers=args.num_layers,
-        dim_feedforward=args.dim_feedforward,
-        time_embed_dim=args.time_embed_dim,
+    model_config = DiffusionTransformerConfig(
+        max_timesteps=cfg.timesteps,
+        max_grid_size=cfg.max_grid_size,
+        d_model=cfg.d_model,
+        num_heads=cfg.num_heads,
+        num_layers=cfg.num_layers,
+        dim_feedforward=cfg.dim_feedforward,
+        time_embed_dim=cfg.time_embed_dim,
     )
-    model = DiffusionTransformer(config).to(device)
+    model = DiffusionTransformer(model_config).to(device)
     total_params = sum(p.numel() for p in model.parameters())
     print(f"Model parameters: {total_params/1e6:.2f}M")
-    #if not args.skip_param_check and not (6.5e6 <= total_params <= 7.5e6):
-    #    raise RuntimeError("Model parameter count deviates from 7M target")
 
-    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
-    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
-    scaler = torch.cuda.amp.GradScaler(enabled=args.mixed_precision)
+    optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=cfg.epochs)
+    scaler = torch.cuda.amp.GradScaler(enabled=cfg.mixed_precision)
 
     ema_model = None
-    if args.ema > 0:
-        ema_model = DiffusionTransformer(config).to(device)
+    if cfg.ema > 0:
+        ema_model = DiffusionTransformer(model_config).to(device)
         ema_model.load_state_dict(model.state_dict())
 
-    if args.resume:
-        state = torch.load(args.resume, map_location=device)
+    if cfg.resume:
+        state = torch.load(cfg.resume, map_location=device)
         model.load_state_dict(state["model"])
         optimizer.load_state_dict(state["optimizer"])
         scheduler.load_state_dict(state["scheduler"])
         if ema_model is not None and "ema" in state:
             ema_model.load_state_dict(state["ema"])
-        print(f"Resumed from {args.resume}")
+        print(f"Resumed from {cfg.resume}")
 
-    schedule = build_diffusion_schedule(args.timesteps, device=device)
+    schedule = build_diffusion_schedule(cfg.timesteps, device=device)
 
-    for epoch in range(1, args.epochs + 1):
+    for epoch in range(1, cfg.epochs + 1):
         model.train()
         epoch_loss = 0.0
         for step, batch in enumerate(train_loader, start=1):
             batch = to_device(batch, device)
             optimizer.zero_grad(set_to_none=True)
-            with torch.cuda.amp.autocast(enabled=args.mixed_precision):
-                loss = compute_loss(model, batch, schedule, args.duality_weight)
+            with torch.cuda.amp.autocast(enabled=cfg.mixed_precision):
+                loss = compute_loss(model, batch, schedule, cfg.duality_weight)
             scaler.scale(loss).backward()
-            if args.grad_clip > 0:
+            if cfg.grad_clip > 0:
                 scaler.unscale_(optimizer)
-                torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
+                torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.grad_clip)
             scaler.step(optimizer)
             scaler.update()
             epoch_loss += loss.item()
-            if args.ema > 0:
-                update_ema(model, ema_model, args.ema)
-            if step % args.log_interval == 0:
+            if cfg.ema > 0 and ema_model is not None:
+                update_ema(model, ema_model, cfg.ema)
+            if step % cfg.log_interval == 0:
                 print(f"Epoch {epoch} Step {step}: loss={loss.item():.4f}")
         scheduler.step()
         avg_loss = epoch_loss / max(1, len(train_loader))
-        val_loss = evaluate(model, val_loader, schedule, args.duality_weight, device)
+        val_loss = evaluate(model, val_loader, schedule, cfg.duality_weight, device)
         print(f"Epoch {epoch}: train_loss={avg_loss:.4f} val_loss={val_loss:.4f}")
-        if epoch % args.save_interval == 0:
-            save_path = Path(args.output_dir) / f"checkpoint_{epoch}.pt"
+        if epoch % cfg.save_interval == 0:
+            save_path = Path(cfg.output_dir) / f"checkpoint_{epoch}.pt"
             save_checkpoint(model, optimizer, scheduler, ema_model, save_path)
             print(f"Saved checkpoint to {save_path}")
 
-    final_path = Path(args.output_dir) / "final_model.pt"
+    final_path = Path(cfg.output_dir) / "final_model.pt"
     save_checkpoint(model, optimizer, scheduler, ema_model, final_path)
     print(f"Training completed, model saved to {final_path}")
 
@@ -256,5 +266,11 @@ def save_checkpoint(
     torch.save(payload, path)
 
 
+def _main_cli(argv: Sequence[str]) -> None:
+    if len(argv) != 1:
+        raise SystemExit("Usage: python train_diffusion_arc.py <config.yaml>")
+    main(Path(argv[0]))
+
+
 if __name__ == "__main__":
-    main()
+    _main_cli(sys.argv[1:])

From a6eec016bf810d80f4776263258536c7eb9a0786 Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 14:34:30 +0100
Subject: [PATCH 11/13] Document YAML command in dataset instructions

---
 README.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/README.md b/README.md
index 4c0c1b7..1b91fc4 100644
--- a/README.md
+++ b/README.md
@@ -13,6 +13,9 @@ cd data
 curl -L https://github.com/fchollet/ARC/archive/refs/heads/master.zip -o arc.zip
 unzip arc.zip 'ARC-AGI-master/data/*'
 cd ..
+
+# Run training with a YAML config (see the Configuration section below)
+python train_diffusion_arc.py path/to/config.yaml
 ```
 
 Any mirror with the same folder structure will also work. The `ARCTaskDataset` loader simply walks every `*.json` file inside the specified split directory.

From 0ce228f0adaf9887903ec8336510f974b4c860ef Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 15:19:07 +0100
Subject: [PATCH 12/13] Improve YAML config validation and documentation

---
 README.md              | 26 +++++++-------
 train_diffusion_arc.py | 80 ++++++++++++++++++++++++++++--------------
 2 files changed, 66 insertions(+), 40 deletions(-)

diff --git a/README.md b/README.md
index 1b91fc4..fb3d87e 100644
--- a/README.md
+++ b/README.md
@@ -42,22 +42,22 @@ Create a YAML file describing your run. Every field has a sensible default excep
 
 | Field | Description |
 | --- | --- |
-| `data_dir` | Path to the ARC dataset root containing `training/` and `evaluation/` folders. |
-| `output_dir` | Directory where checkpoints and the final model will be written. |
-| `batch_size` | Batch size for both training and validation loaders. |
-| `epochs` | Number of full passes over the training set. |
-| `lr` / `weight_decay` | AdamW optimizer hyper-parameters. |
-| `timesteps` | Number of diffusion steps in the schedule. |
-| `val_fraction` | Fraction of the dataset used for validation. |
+| `data_dir` | Path to the ARC dataset root containing `training/` and `evaluation/` folders. The directory must exist. |
+| `output_dir` | Directory where checkpoints and the final model will be written (created automatically when missing). |
+| `batch_size` | Batch size for both training and validation loaders (must be ≥ 1). |
+| `epochs` | Number of full passes over the training set (must be ≥ 1). |
+| `lr` / `weight_decay` | AdamW optimizer hyper-parameters (learning rate must be > 0). |
+| `timesteps` | Number of diffusion steps in the schedule (must be ≥ 1). |
+| `val_fraction` | Fraction of the dataset used for validation. Values > 0 reserve at least one example when possible and must be < 1. |
 | `seed` | Random seed for Python, PyTorch and data splits. |
 | `grad_clip` | Gradient clipping value (set to `0` to disable). |
 | `device` | Device string understood by `torch.device`, defaults to `cuda` when available. |
-| `ema` | Exponential moving average decay for model weights (disabled when `0`). |
-| `duality_weight` | Weight applied to the clean target reconstruction loss term. |
-| `log_interval` | Number of training steps between log messages. |
-| `num_workers` | Data loader worker count. |
-| `save_interval` | Save a checkpoint every N epochs. |
-| `resume` | Optional path to a checkpoint to resume from. |
+| `ema` | Exponential moving average decay for model weights (`0` disables EMA, must be between `0` and `1`). |
+| `duality_weight` | Weight applied to the clean target reconstruction loss term (must be ≥ 0). |
+| `log_interval` | Number of training steps between log messages (must be ≥ 1). |
+| `num_workers` | Data loader worker count (must be ≥ 0). |
+| `save_interval` | Save a checkpoint every N epochs (must be ≥ 1). |
+| `resume` | Optional path to a checkpoint to resume from. The file must exist when provided. |
 | `augment` | Enable random grid flips during dataset loading. |
 | `mixed_precision` | Enable automatic mixed precision training. |
 | `max_grid_size`, `d_model`, `num_heads`, `num_layers`, `dim_feedforward`, `time_embed_dim` | Architectural parameters passed to `DiffusionTransformerConfig`. |
diff --git a/train_diffusion_arc.py b/train_diffusion_arc.py
index 9c40699..f172d55 100644
--- a/train_diffusion_arc.py
+++ b/train_diffusion_arc.py
@@ -3,7 +3,6 @@
 
 from __future__ import annotations
 
-import os
 import sys
 from pathlib import Path
 from typing import Dict, Sequence
@@ -11,7 +10,7 @@
 import torch
 from torch.utils.data import DataLoader, random_split
 
-from pydantic import Field
+from pydantic import Field, model_validator
 from pydantic_config import SettingsConfig, SettingsModel
 from pydantic_config.main import ConfigFileSettingsSource
 
@@ -32,32 +31,49 @@ class DiffusionArcTrainingConfig(SettingsModel):
 
     data_dir: Path
     output_dir: Path = Path("outputs/diffusion_arc")
-    batch_size: int = 32
-    epochs: int = 50
-    lr: float = 3e-4
-    weight_decay: float = 0.01
-    timesteps: int = 1000
-    val_fraction: float = 0.1
+    batch_size: int = Field(32, ge=1)
+    epochs: int = Field(50, ge=1)
+    lr: float = Field(3e-4, gt=0)
+    weight_decay: float = Field(0.01, ge=0)
+    timesteps: int = Field(1000, ge=1)
+    val_fraction: float = Field(0.1, ge=0.0, lt=1.0)
     seed: int = 42
-    grad_clip: float = 1.0
+    grad_clip: float = Field(1.0, ge=0.0)
     device: str = Field(default_factory=_default_device)
-    ema: float = 0.0
-    duality_weight: float = 0.5
-    log_interval: int = 100
-    num_workers: int = 2
-    save_interval: int = 5
+    ema: float = Field(0.0, ge=0.0, le=1.0)
+    duality_weight: float = Field(0.5, ge=0.0)
+    log_interval: int = Field(100, ge=1)
+    num_workers: int = Field(2, ge=0)
+    save_interval: int = Field(5, ge=1)
     resume: Path | None = None
     augment: bool = False
     mixed_precision: bool = False
-    max_grid_size: int = 30
-    d_model: int = 288
-    num_heads: int = 8
-    num_layers: int = 7
-    dim_feedforward: int = 1152
-    time_embed_dim: int = 512
+    max_grid_size: int = Field(30, ge=1)
+    d_model: int = Field(288, ge=1)
+    num_heads: int = Field(8, ge=1)
+    num_layers: int = Field(7, ge=1)
+    dim_feedforward: int = Field(1152, ge=1)
+    time_embed_dim: int = Field(512, ge=1)
 
     model_config = SettingsConfig(extra="forbid")
 
+    @model_validator(mode="after")
+    def _normalise_paths(self) -> "DiffusionArcTrainingConfig":
+        self.data_dir = self.data_dir.expanduser()
+        if not self.data_dir.exists():
+            raise ValueError(f"data_dir does not exist: {self.data_dir}")
+        if not self.data_dir.is_dir():
+            raise ValueError(f"data_dir must be a directory: {self.data_dir}")
+
+        self.output_dir = self.output_dir.expanduser()
+
+        if self.resume is not None:
+            self.resume = self.resume.expanduser()
+            if not self.resume.exists():
+                raise ValueError(f"resume checkpoint not found: {self.resume}")
+
+        return self
+
     @classmethod
     def from_yaml(cls, path: Path | str) -> "DiffusionArcTrainingConfig":
         source = ConfigFileSettingsSource(
@@ -96,7 +112,7 @@ def main(config: DiffusionArcTrainingConfig | Path | str) -> None:
     set_seed(cfg.seed)
 
     device = torch.device(cfg.device)
-    os.makedirs(cfg.output_dir, exist_ok=True)
+    cfg.output_dir.mkdir(parents=True, exist_ok=True)
 
     dataset = ARCTaskDataset(
         cfg.data_dir,
@@ -104,20 +120,30 @@ def main(config: DiffusionArcTrainingConfig | Path | str) -> None:
         max_grid_size=cfg.max_grid_size,
         augment=cfg.augment,
     )
-    val_size = max(1, int(len(dataset) * cfg.val_fraction))
-    train_size = len(dataset) - val_size
+    total_items = len(dataset)
+    if total_items == 0:
+        raise RuntimeError(f"No ARC tasks found in {cfg.data_dir}")
+
+    val_size = int(total_items * cfg.val_fraction)
+    if cfg.val_fraction > 0 and val_size == 0 and total_items > 1:
+        val_size = 1
+    if val_size >= total_items:
+        val_size = total_items - 1
+    train_size = total_items - val_size
+
     generator = torch.Generator().manual_seed(cfg.seed)
     train_dataset, val_dataset = random_split(
         dataset, [train_size, val_size], generator=generator,
     )
 
+    pin_memory = device.type == "cuda"
     train_loader = DataLoader(
         train_dataset,
         batch_size=cfg.batch_size,
         shuffle=True,
         collate_fn=arc_collate,
         num_workers=cfg.num_workers,
-        pin_memory=True,
+        pin_memory=pin_memory,
     )
     val_loader = DataLoader(
         val_dataset,
@@ -125,7 +151,7 @@ def main(config: DiffusionArcTrainingConfig | Path | str) -> None:
         shuffle=False,
         collate_fn=arc_collate,
         num_workers=cfg.num_workers,
-        pin_memory=True,
+        pin_memory=pin_memory,
     )
 
     model_config = DiffusionTransformerConfig(
@@ -185,11 +211,11 @@ def main(config: DiffusionArcTrainingConfig | Path | str) -> None:
         val_loss = evaluate(model, val_loader, schedule, cfg.duality_weight, device)
         print(f"Epoch {epoch}: train_loss={avg_loss:.4f} val_loss={val_loss:.4f}")
         if epoch % cfg.save_interval == 0:
-            save_path = Path(cfg.output_dir) / f"checkpoint_{epoch}.pt"
+            save_path = cfg.output_dir / f"checkpoint_{epoch}.pt"
             save_checkpoint(model, optimizer, scheduler, ema_model, save_path)
             print(f"Saved checkpoint to {save_path}")
 
-    final_path = Path(cfg.output_dir) / "final_model.pt"
+    final_path = cfg.output_dir / "final_model.pt"
     save_checkpoint(model, optimizer, scheduler, ema_model, final_path)
     print(f"Training completed, model saved to {final_path}")
 

From ac81de9b310378157947aa433fd09111899a4527 Mon Sep 17 00:00:00 2001
From: Tom Pollak <tompollak1000@gmail.com>
Date: Fri, 10 Oct 2025 15:21:25 +0100
Subject: [PATCH 13/13] Resolve config paths relative to YAML file

---
 README.md                         |  3 +++
 tests/test_train_diffusion_arc.py |  6 +++---
 train_diffusion_arc.py            | 36 ++++++++++++++++++++++++-------
 3 files changed, 34 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index fb3d87e..fdeeaca 100644
--- a/README.md
+++ b/README.md
@@ -62,6 +62,9 @@ Create a YAML file describing your run. Every field has a sensible default excep
 | `mixed_precision` | Enable automatic mixed precision training. |
 | `max_grid_size`, `d_model`, `num_heads`, `num_layers`, `dim_feedforward`, `time_embed_dim` | Architectural parameters passed to `DiffusionTransformerConfig`. |
 
+Relative paths are resolved from the directory that contains the YAML file, so a configuration can live alongside the data and checkpoints.
+Absolute paths continue to work as usual.
+
 Example configuration:
 
 ```yaml
diff --git a/tests/test_train_diffusion_arc.py b/tests/test_train_diffusion_arc.py
index a82fdc8..78433b5 100644
--- a/tests/test_train_diffusion_arc.py
+++ b/tests/test_train_diffusion_arc.py
@@ -39,9 +39,9 @@ def test_main_runs_with_tiny_model(tmp_path: Path) -> None:
     config_path = tmp_path / "config.yaml"
     config_path.write_text(
         textwrap.dedent(
-            f"""
-            data_dir: {data_dir}
-            output_dir: {output_dir}
+            """
+            data_dir: arc
+            output_dir: outputs
             batch_size: 1
             epochs: 1
             timesteps: 4
diff --git a/train_diffusion_arc.py b/train_diffusion_arc.py
index f172d55..81d4863 100644
--- a/train_diffusion_arc.py
+++ b/train_diffusion_arc.py
@@ -5,7 +5,7 @@
 
 import sys
 from pathlib import Path
-from typing import Dict, Sequence
+from typing import ClassVar, Dict, Sequence
 
 import torch
 from torch.utils.data import DataLoader, random_split
@@ -29,6 +29,8 @@ def _default_device() -> str:
 class DiffusionArcTrainingConfig(SettingsModel):
     """Configuration for diffusion transformer ARC training."""
 
+    _base_dir: ClassVar[Path | None] = None
+
     data_dir: Path
     output_dir: Path = Path("outputs/diffusion_arc")
     batch_size: int = Field(32, ge=1)
@@ -57,32 +59,50 @@ class DiffusionArcTrainingConfig(SettingsModel):
 
     model_config = SettingsConfig(extra="forbid")
 
+    @staticmethod
+    def _resolve_path(value: Path, base_dir: Path | None) -> Path:
+        value = value.expanduser()
+        if not value.is_absolute():
+            base = base_dir or Path.cwd()
+            value = (base / value).expanduser()
+        return value
+
     @model_validator(mode="after")
     def _normalise_paths(self) -> "DiffusionArcTrainingConfig":
-        self.data_dir = self.data_dir.expanduser()
+        base_dir = self.__class__._base_dir
+
+        self.data_dir = self._resolve_path(self.data_dir, base_dir)
         if not self.data_dir.exists():
             raise ValueError(f"data_dir does not exist: {self.data_dir}")
         if not self.data_dir.is_dir():
             raise ValueError(f"data_dir must be a directory: {self.data_dir}")
 
-        self.output_dir = self.output_dir.expanduser()
+        self.output_dir = self._resolve_path(self.output_dir, base_dir)
 
         if self.resume is not None:
-            self.resume = self.resume.expanduser()
-            if not self.resume.exists():
-                raise ValueError(f"resume checkpoint not found: {self.resume}")
+            resume_path = self._resolve_path(self.resume, base_dir)
+            if not resume_path.exists():
+                raise ValueError(f"resume checkpoint not found: {resume_path}")
+            self.resume = resume_path
 
         return self
 
     @classmethod
     def from_yaml(cls, path: Path | str) -> "DiffusionArcTrainingConfig":
+        config_path = Path(path)
+        if not config_path.exists():
+            raise FileNotFoundError(f"Configuration file not found: {config_path}")
         source = ConfigFileSettingsSource(
             cls,
-            config_file=Path(path),
+            config_file=config_path,
             config_file_required=True,
         )
         data = source()
-        return cls.model_validate(data)
+        cls._base_dir = config_path.resolve().parent
+        try:
+            return cls.model_validate(data)
+        finally:
+            cls._base_dir = None
 
 
 def set_seed(seed: int) -> None: