PIPer: On-Device Environment Setup via Online Reinforcement Learning
Authors: name: Alexander Kovrigin; name: Aleksandra Eliseeva; name: Konstantin Grotov; name: Egor Bogomolov; name: Yaroslav Zharov
Published: 2025-09-29 20:03:05

Abstract: Environment setup-the process of configuring the system to work with a
specific software project-represents a persistent challenge in Software
Engineering (SE). Automated environment setup methods could assist developers
by providing fully configured environments for arbitrary repositories without
manual effort. This also helps SE researchers to scale execution-based
benchmarks. However, recent studies reveal that even state-of-the-art Large
Language Models (LLMs) achieve limited success in automating this task. To
address this limitation, we tune a specialized model for environment setup. We
combine supervised fine-tuning for generating correct Bash scripts and
Reinforcement Learning with Verifiable Rewards (RLVR) to adapt it to the task
of environment setup. On EnvBench-Python, our method enables Qwen3-8B (a model
runnable on consumer hardware) to perform on par with larger models-Qwen3-32B
and GPT-4o. The training code and model checkpoints are available online:
https://github.com/JetBrains-Research/PIPer.