Libra: Assessing and Improving Reward Model by Learning to Think

• URL: http://arxiv.org/abs/2507.21645v1
• Date: Tue, 29 Jul 2025 10:02:43 GMT
• Title: Libra: Assessing and Improving Reward Model by Learning to Think
• Authors: Meng Zhou, Bei Li, Jiahao Liu, Xiaowen Shi, Yang Bai, Rongxiang Weng, Jingang Wang, Xunliang Cai,
• Abstract summary: We present a reasoning-oriented benchmark (Libra Bench) to address the limitations of existing reward model benchmarks in reasoning scenarios.<n>We introduce a novel approach for improving the generative reward model via learning-to-think methodologies.<n>We develop Libra-RM series, a collection of generative reward models with reasoning capabilities that achieve state-of-the-art results on various benchmarks.
• Score: 37.22776255575947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL) has significantly improved the reasoning ability of large language models. However, current reward models underperform in challenging reasoning scenarios and predominant RL training paradigms rely on rule-based or reference-based rewards, which impose two critical limitations: 1) the dependence on finely annotated reference answer to attain rewards; and 2) the requirement for constrained output format. These limitations fundamentally hinder further RL data scaling and sustained enhancement of model reasoning performance. To address these limitations, we propose a comprehensive framework for evaluating and improving the performance of reward models in complex reasoning scenarios. We first present a reasoning-oriented benchmark (Libra Bench), systematically constructed from a diverse collection of challenging mathematical problems and advanced reasoning models, to address the limitations of existing reward model benchmarks in reasoning scenarios. We further introduce a novel approach for improving the generative reward model via learning-to-think methodologies. Based on the proposed approach, we develop Libra-RM series, a collection of generative reward models with reasoning capabilities that achieve state-of-the-art results on various benchmarks. Comprehensive downstream experiments are conducted and the experimental results demonstrate the correlation between our Libra Bench and downstream application, and the potential of Libra-RM to further improve reasoning models with unlabeled data.

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