Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning

• URL: http://arxiv.org/abs/2508.03501v1
• Date: Tue, 05 Aug 2025 14:30:47 GMT
• Title: Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning
• Authors: Alexander Golubev, Maria Trofimova, Sergei Polezhaev, Ibragim Badertdinov, Maksim Nekrashevich, Anton Shevtsov, Simon Karasik, Sergey Abramov, Andrei Andriushchenko, Filipp Fisin, Sergei Skvortsov, Boris Yangel,
• Abstract summary: We train an agent based on Qwen2.5-72B-Instruct to solve real-world software engineering tasks.<n>Our approach increases the agent's success rate on the SWE-bench Verified benchmark from a 20% fine-tuned baseline to 39%.
• Score: 31.540626068273014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research on applications of Reinforcement Learning (RL) to Large Language Models (LLMs) has mostly been focused on single-turn problems, such as mathematical reasoning or single-shot code generation. While these problems can be viewed as token-level multi-turn MDPs, this view corresponds to a degenerate case of multi-turn interaction where the environment provides no feedback. This contrasts with many real-world domains, such as software engineering (SWE), which require rich multi-turn interactions with a stateful environment that responds to each action with a non-trivial observation. To bridge this gap, we demonstrate the successful application of RL to this general regime. Using a modified Decoupled Advantage Policy Optimization (DAPO) algorithm, we train an agent based on Qwen2.5-72B-Instruct to solve real-world software engineering tasks. Our approach increases the agent's success rate on the SWE-bench Verified benchmark from a 20% rejection fine-tuned baseline to 39%, without relying on any teacher models. On SWE-rebench, our agent matches or outperforms leading open-weight models such as DeepSeek-V3-0324 and Qwen3-235B-A22B using an identical scaffolding, offering a viable path toward building more capable autonomous agents for complex real-world problems based on open models.

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