README.md

metadata

title: OpenSleuth Colab
emoji: 🕵️
colorFrom: indigo
colorTo: green
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0

OpenSleuth — Colab quickstart Space

This Space is a thin landing page for the train_opensleuth_grpo.ipynb notebook — the minimum reproducible Colab for training an OpenSleuth agent end-to-end against the live env Space.

What is OpenSleuth?

An Algorithmic Detective RL environment. An LLM agent reverse-engineers an unknown black-box Python function by probing it with inputs and then submitting a Python replica. The environment scores submissions by domain-aware fuzz-testing against the hidden reference, with a complexity penalty so the agent can't just memorise its probes inside a giant if/else.

Try it

Click the badge to open the notebook in Google Colab:

Or download train_opensleuth_grpo.ipynb from the Files tab and upload it to Colab manually. Set the runtime to GPU → T4 and hit Runtime → Run all — end-to-end training completes in roughly 15 – 25 minutes on a free-tier T4 with the default Qwen2.5-0.5B-Instruct config.

What the notebook does

1. Pip-installs the pinned trainer stack (transformers==4.51.3, trl==0.16.1, peft==0.14.0, accelerate==1.4.0, bitsandbytes==0.45.5, datasets==3.3.2).
2. Hits the live env Space anugrah55/opensleuth-env-gemini-cli at https://anugrah55-opensleuth-env-gemini-cli.hf.space to discover all 15 tasks (9 builtins + 6 from the Hub task dataset).
3. Builds a synthesis dataset where each row is (signature + observed probes) → expected python implementation.
4. Loads Qwen2.5-0.5B-Instruct in 4-bit + LoRA so it fits on a T4.
5. Trains with HF TRL's GRPOTrainer using a two-part reward:
   • env-verifier reward: real fuzz-tested correctness against the hidden reference, with a complexity penalty.
   • format reward: tiny shaping signal for emitting a fenced python code block with the right function name.
6. Optionally pushes the trained LoRA adapter to your own Hub account.
7. Runs a 3-episode smoke eval and prints the agent's emitted code.

Links

• Env Space (REST API the notebook calls): https://huggingface.co/spaces/anugrah55/opensleuth-env-gemini-cli
• Training Space (full 3B retrain): https://huggingface.co/spaces/anugrah55/opensleuth-training-gemini-cli
• Open-ended task catalog (Hub dataset): https://huggingface.co/datasets/anugrah55/opensleuth-tasks

License

Apache-2.0.