JetBrains-Research/PIPer-8B-RL-only

<img src="https://github.com/JetBrains-Research/PIPer/blob/main/misc/piper-logo.png?raw=true" alt="PIPer Mascot" style="height: 6em"> <h1> PIPer: On-Device Environment Setup via Online Reinforcement Learning

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Democratizing environment setup with on-device sized models that match the performance of much larger proprietary systems

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🎯 Overview

Environment setup—the process of configuring systems to work with specific software projects—remains a persistent challenge in software engineering. PIPer addresses this by training specialized on-device models that can automatically generate correct Bash scripts for environment configuration.

Our approach combines:

• —📚 Supervised Fine-Tuning (SFT) with executable scripts from larger models
• —🎯 Reinforcement Learning with Verifiable Rewards (RLVR) using lightweight proxy LLM-reward

🏆 Key Results

🎉 PIPer achieves 9× improvement over its base model while matching GPT-4o performance at 25x lower cost

📦 Available Artifacts

🤖 Model Checkpoints

📊 Datasets

🚀 Reproduce the results

We use uv for dependency management and Ray for distributed training.

git clone https://github.com/JetBrains-Research/PIPer.git
cd PIPer
git submodule update --init --recursive
uv sync

To run the experiments, you need a node with at least 4 H200 GPUs and Ray installed and running. Then you can run all the experiments with the following command:

uv run piper/hparams_entrypoint.py --multirun +experiment==llm-reward

You can look up the experiment Hydra configurations in piper/config/ folder, or print out the whole config with the following command:

uv run piper/hparams_entrypoint.py +experiment=llm-reward --info config

📊 Evaluation Benchmarks

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.