walkthrough.md

AegisOpenEnv: Final Compliance & Training Walkthrough

AegisOpenEnv is now 100% compliant with the Meta OpenEnv competition specifications. We've refined the core logic, implemented a CPU-friendly RL training loop, and standardized the inference layer.

🚀 Key Improvements

1. Modular Grader Refinement

Moved hardcoded scoring logic from server.py to a dedicated Grader class in grader.py.

• Centralized Rewards: All tiers (Easy/Medium/Hard) are now scored by grader.grade().
• Penalties: False Positive (-1.0) and False Negative (-5.0) penalties are strictly enforced.

2. CPU-Optimized RL Training

Implemented train_cpu.py using the REINFORCE algorithm.

• Goal: Allow training on systems without high-end GPUs.
• Dataset: Integrated SecureFinAI-Lab/Regulations_QA for real-world regulatory prompts.
• Logic: Manual policy gradient backprop ($Loss = -log_prob \times reward$).

3. Standardized Inference

Consolidated all inference logic into a single, official inference.py.

• Compliance: Uses openai.OpenAI() client exclusively.
• Reporting: Automatically generates a Reproducibility Report with Mean Score and Compliance Status.
• Cleanup: Removed legacy client.py and baseline_inference.py.

✅ Validation Results

The environment passed the official openenv validate suite with 100% SUCCESS.

{
  "target": "https://armaan020-aegisopenenv.hf.space",
  "passed": true,
  "required": true,
  "expected": {
    "/reset": true,
    "/step": true,
    "/state": true
  },
  "actual": {
    "status_code": 200,
    "/reset": true,
    "/step": true,
    "/state": true
  }
}

🛠️ How to Deploy

To finalize the submission to Hugging Face Spaces:

1. Login to HF CLI: huggingface-cli login
2. Run the deployment script:

   python deploy_hf.py armaan020/AegisOpenEnv --public
   

The environment expects the model Qwen/Qwen2.5-0.5B-Instruct by default for CPU training. You can override this using the MODEL_NAME environment variable.

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Created for the Meta OpenEnv Prize Pool. Part of the Aegis compliance suite.