PIPer-iclr/PIPer-8B-RL-only

🚀 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

📊 Evaluation Benchmarks

🙏 Acknowledgments

• —Built on top of EnvBench evaluation framework
• —Uses VeRL for efficient RL training
• —Leverages Qwen3 model family as base architecture

📄 License

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