CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models
This work proposes CodeScaler, a reward model designed to scale both reinforcement learning training and test-time inference for code generation, and surpasses existing reward models on RM-Bench not only in the code domain, but also in general and reasoning domains.
Xiao Zhu, Xinyu Zhou, Boyuan Zhu et al.
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