GAPO : Robust Advantage Estimation for Real-World Code LLM s
Jianqing Zhang · Zhezheng Hao · Wei Xia · Hande Dong · Hong Wang · Chenxing Wei · Yuyan Zhou · Yubin Qi · Qiang Lin · Jian Cao
摘要
Reinforcement learning (RL) is widely used for post-training large language models (LLMs) in code editing, where group-relative methods, such as GRPO, are popular due to their critic-free and normalized advantage estimation. However, in real-world code-editing scenarios, reward distributions are...