A^2Agent: Action-Aware Reinforcement Learning for Repository-Level Code Localization Agents
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 is proposed.
Doyeon Kim, Suyoung Bae, Yumin Lee et al.
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