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Fast-Slow Thinking RM: Efficient Integration of Scalar and Generative Reward Models

Wu, Jiayun et al.
arXiv (All) · Papers
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computation-and-language
Reward models (RMs) are critical for aligning Large Language Models via Reinforcement Learning from Human Feedback (RLHF). While Generative Reward Models (GRMs) achieve superior accuracy through chain-of-thought (CoT) reasoning, they incur substantial computational costs. Conversely, Scalar Reward M
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