PIPer: On-Device Environment Setup via Online Reinforcement Learning
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.
68dd50e30898d7e69e16c9c7
Alexander Kovrigin
claimed_verified
2025-10-02T03:08:27.923Z
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Alexander Kovrigin
user
waleko
68dd50e30898d7e69e16c9c8
Aleksandra Eliseeva
claimed_verified
2025-10-02T03:08:23.716Z
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Aleksandra Eliseeva
user
saridormi
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Konstantin Grotov
claimed_verified
2025-10-02T03:08:31.102Z
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Konstantin Grotov
user
konstantgr
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Egor Bogomolov
claimed_verified
2025-10-02T13:56:17.621Z
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Egor Bogomolov
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egor-bogomolov
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Yaroslav Zharov
claimed_verified
2025-10-07T12:29:15.218Z
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Yaroslav
user
yaroslav-zharov
PIPer: On-Device Environment Setup via Online Reinforcement Learning
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.
A specialized model combining supervised fine-tuning and Reinforcement Learning with Verifiable Rewards achieves competitive performance in automated environment setup tasks.
Large Language Models LLMs supervised fine-tuning Reinforcement Learning with Verifiable Rewards RLVR Bash scripts EnvBench-Python Qwen3-8B Qwen3-32B GPT-4o