Complete Environment Snapshot & Zero-Training sections

[carabistouflette]

PR Type

• RL Environment PR - Complete Environment Snapshot & Zero-Training sections
• Non-Environment PR - Complete Description, Related Issues & Type of Change sections

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📝 General Information

Description

This PR continues the work started in PR #258 to create a Verifiers Environment (VerifiersEnv) that integrates the external verifiers library into the Atropos ecosystem.

Key Changes:

• Refactored Architecture: Moved the environment implementation from environments/ to atroposlib/envs/ to align with the project's module structure.
• Robust Error Handling: Added import guards for the optional verifiers dependency, defensive checks for empty API responses and None content, and division-by-zero protection for reward weight normalization.
• W&B Tracking: Implemented percent_correct and cumulative accuracy metrics for Weights & Biases logging.
• Configurable Thresholds: Added an optional reward_threshold config option for binary reward conversion.
• Compatibility Layer: Created a _call_reward_func wrapper to handle API differences across verifiers versions.
• Full Test Coverage: Added a comprehensive test suite (test_verifiers.py) covering initialization, trajectory collection, and item fetching.

Related Issues

Continues PR #258

Type of Change

• New feature (non-breaking change which adds functionality)
• This change requires a documentation update

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🔖 Environment Snapshot

Field	Your Entry
Environment Name	VerifiersEnv
Short Description	Wrapper for loading and running RL tasks from the verifiers library ecosystem.
Category	Verifiable-Reasoning
Dataset Needed?	No (datasets provided by the verifiers library dynamically)
External Deps	verifiers (pip install verifiers)
Environmental Variables	None
Compute Footprint Estimate	<1 GB RAM, standard CPU/GPU for inference

🧪 Zero-Training Test Results

Details

W&B Link: N/A - Requires external verifiers package with specific environment configuration.

Examples of the Environment scoring a good example and a bad example:
Tested via unit tests in test_verifiers.py. Mocked environment correctly scores valid responses with 1.0 and handles empty/error responses gracefully.

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✅ Developer & Reviewer Checklist

• Code follows project style (black, isort, flake8 pass with pre-commit)
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• New and existing unit tests pass locally with my changes
• Docstrings added for all new public classes / functions
• If .env vars required, did you add it to the .env.example in repo root?

[carabistouflette]

pre-commit.ci seems to be complaining about a comment in example_trainer/vllm_api_server.py
I didn't touch this file (check files changed) so it's safe to ignore it.