Learning Adaptive Parallel Execution for Efficient Code Localization

• URL: http://arxiv.org/abs/2601.19568v1
• Date: Tue, 27 Jan 2026 12:59:31 GMT
• Title: Learning Adaptive Parallel Execution for Efficient Code Localization
• Authors: Ke Xu, Siyang Xiao, Ming Liang, Yichen Yu, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li,
• Abstract summary: Current code localization agents demonstrate a 34.9% redundant invocation rate.<n>We propose textbfFuseSearch, reformulating parallel code localization as a textbfjoint quality-efficiency optimization task.
• Score: 18.27024381786272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code localization constitutes a key bottleneck in automated software development pipelines. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9\% redundant invocation rate, which negates parallelism benefits. We propose \textbf{FuseSearch}, reformulating parallel code localization as a \textbf{joint quality-efficiency optimization} task. Through defining \textbf{tool efficiency} -- the ratio of unique information gain to invocation count -- we utilize a two-phase SFT and RL training approach for learning adaptive parallel strategies. Different from fixed-breadth approaches, FuseSearch dynamically modulates search breadth according to task context, evolving from exploration phases to refinement stages. Evaluated on SWE-bench Verified, FuseSearch-4B achieves SOTA-level performance (84.7\% file-level and 56.4\% function-level $F_1$ scores) with 93.6\% speedup, utilizing 67.7\% fewer turns and 68.9\% fewer tokens. Results indicate that efficiency-aware training naturally improves quality through eliminating noisy redundant signals, enabling high-performance cost-effective localization agents.

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