Rapid advances in large language models have made software engineering agents capable of tackling complex coding tasks. Within a containerized environment, a coding agent receives tool responses or execution results, and determines its later actions by those earlier results. However, most existing coding agents never l...
Yi-Rou Ge, Kang-Rui Ruan, Ji-Fan Chen et al.· 0 citations
Reinforcement learning with verifiable rewards improves the reasoning capabilities of large language models, but remains computationally expensive and sample inefficient. On-policy distillation (OPD) provides dense token-level supervision from a stronger teacher, yet standard OPD distills every token regardless of whet...
Yilan Chen, Zian Su, Manish Prajapat et al.· 0 citations
Group Adaptive Clipping Policy Optimization (GAPO), a plug-in modification to GRPO methods that adapts the clipping boundary to the rollout advantage, is proposed, motivated by a reverse-KL trust-region perspective, which suggests that rollouts with larger learning signal should receive proportionally greater update he...
Sheng Jia, Xiao Wang, S. Kasiviswanathan et al.· 0 citations
Results from a large-scale A/B test comparing GenRec against the current production ranker model are reported, where it is shown that a GenRec model trained with substantially fewer Phase-2 labeled training examples and input signals can achieve statistically significant gains in offline and online metrics.
Ying Li, Shradha Sehgal, A. Rao et al.· 0 citations
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