Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty
Recent advancements in the Generative Reward Model (GRM) have demonstrated its potential to enhance the reasoning abilities of LLMs through Chain-of-Thought (CoT) prompting. Despite these gains, existing implementations of GRM suffer from two critical limitations. First, CoT prompting is applied indiscriminately to all inputs regardless of their inherent complexity. This introduces unnecessary computational costs for tasks amenable to fast, direct inference. Second, existing approaches primarily rely on voting-based mechanisms to evaluate CoT outputs, which often lack granularity and precision in assessing reasoning quality. In this paper, we propose E-GRM, an efficient generative reward ...
| Authors | Chao Xue, Yao Wang, Mengqiao Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 April 2026 |
| Last updated at source | 3 May 2026 |
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