A^2Agent: Action-Aware Reinforcement Learning for Repository-Level Code Localization Agents

Abstract:Localizing issue-relevant code regions is a critical step in automated software engineering. However, due to their reliance on sparse trajectory-level signals, existing methods cannot identify which per-turn actions are effective and often discover correct code regions during exploration but fail to commit them. To address these limitations, we propose an action-aware reinforcement learning method that combines a per-turn reward sequence rewarding both the discovery and commitment of gold code regions with an action-level advantage estimation scheme that isolates each action's credit by grouping turns sharing the same exploration context. Extensive evaluations show that our method improves the average F1 over the state-of-the-art (SOTA) by 1.58% on SWE-Bench Verified and 8.55% on SWE-Bench Pro, with our 4B model outperforming baselines up to 8x larger. Our code is available at this https URL.

Computation and Language (cs.CL); Software Engineering (cs.SE)

arXiv:2608.29831 [cs.CL]

(or arXiv:2608.29831v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.29831 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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