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Xiaotang Shang

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BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models

BoostAPR is presented, a three-stage framework addressing challenges of supervised fine-tuning on execution-verified demonstrations with reasoning traces, training dual reward models from execution outcomes, and PPO optimization where the line-level model redistributes rewards to critical edit regions.

Yuan-Hao Li, Hong-Bo Wang, Xiaotang Shang et al. · 3 citations · ⚡1

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