IRPO: Scaling the Bradley-Terry Model via Reinforcement Learning

• URL: http://arxiv.org/abs/2601.00677v1
• Date: Fri, 02 Jan 2026 12:57:06 GMT
• Title: IRPO: Scaling the Bradley-Terry Model via Reinforcement Learning
• Authors: Haonan Song, Qingchen Xie, Huan Zhu, Feng Xiao, Luxi Xing, Fuzhen Li, Liu Kang, Feng Jiang, Zhiyong Zheng, Fan Yang,
• Abstract summary: Intergroup Relative Preference Optimization (IRPO) is a novel RL framework that incorporates the well-established Bradley-Terry model into GRPO.<n>By generating a pointwise score for each response, IRPO enables efficient evaluation of arbitrarily many candidates during RL training.<n> Experimental results demonstrate that IRPO achieves state-of-the-art (SOTA) performance among pointwise GRMs.
• Score: 11.499402258204375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Reward Models (GRMs) have attracted considerable research interest in reward modeling due to their interpretability, inference-time scalability, and potential for refinement through reinforcement learning (RL). However, widely used pairwise GRMs create a computational bottleneck when integrated with RL algorithms such as Group Relative Policy Optimization (GRPO). This bottleneck arises from two factors: (i) the O(n^2) time complexity of pairwise comparisons required to obtain relative scores, and (ii) the computational overhead of repeated sampling or additional chain-of-thought (CoT) reasoning to improve performance. To address the first factor, we propose Intergroup Relative Preference Optimization (IRPO), a novel RL framework that incorporates the well-established Bradley-Terry model into GRPO. By generating a pointwise score for each response, IRPO enables efficient evaluation of arbitrarily many candidates during RL training while preserving interpretability and fine-grained reward signals. Experimental results demonstrate that IRPO achieves state-of-the-art (SOTA) performance among pointwise GRMs across multiple benchmarks, with performance comparable to that of current leading pairwise GRMs. Furthermore, we show that IRPO significantly outperforms pairwise GRMs in post-training evaluations.

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