Empowering RepoQA-Agent based on Reinforcement Learning Driven by Monte-carlo Tree Search

• URL: http://arxiv.org/abs/2510.26287v1
• Date: Thu, 30 Oct 2025 09:10:36 GMT
• Title: Empowering RepoQA-Agent based on Reinforcement Learning Driven by Monte-carlo Tree Search
• Authors: Guochang Li, Yuchen Liu, Zhen Qin, Yunkun Wang, Jianping Zhong, Chen Zhi, Binhua Li, Fei Huang, Yongbin Li, Shuiguang Deng,
• Abstract summary: We introduce RepoSearch-R1, a novel agentic reinforcement learning framework driven by Monte-carlo Tree Search.<n>Based on RepoSearch-R1, we construct a RepoQA-Agent specifically designed for repository question-answering tasks.
• Score: 70.63903518295785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Repository-level software engineering tasks require large language models (LLMs) to efficiently navigate and extract information from complex codebases through multi-turn tool interactions. Existing approaches face significant limitations: training-free, in-context learning methods struggle to guide agents effectively in tool utilization and decision-making based on environmental feedback, while training-based approaches typically rely on costly distillation from larger LLMs, introducing data compliance concerns in enterprise environments. To address these challenges, we introduce RepoSearch-R1, a novel agentic reinforcement learning framework driven by Monte-carlo Tree Search (MCTS). This approach allows agents to generate diverse, high-quality reasoning trajectories via self-training without requiring model distillation or external supervision. Based on RepoSearch-R1, we construct a RepoQA-Agent specifically designed for repository question-answering tasks. Comprehensive evaluation on repository question-answering tasks demonstrates that RepoSearch-R1 achieves substantial improvements of answer completeness: 16.0% enhancement over no-retrieval methods, 19.5% improvement over iterative retrieval methods, and 33% increase in training efficiency compared to general agentic reinforcement learning approaches. Our cold-start training methodology eliminates data compliance concerns while maintaining robust exploration diversity and answer completeness across repository-level reasoning tasks.

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