Title: Preference-Based Process Reward Model for Robust Mathematical Reasoning
Primary area: foundation or frontier models, including LLMs
Keywords: Process Reward Model, Reinforcement Learning, Monte Carlo Tree Search

Abstract: Process reward models (PRMs) have emerged as a promising approach to guide LLMs by providing step-wise supervision, but traditional methods often rely on heuristic search strategies like Monte Carlo Tree Search (MCTS), which introduce bias and limit generalization. In this work, we propose a reinforcement learning framework guided by a   Preference-Based Process Reward Model (PPRM) , which provides step-wise supervision to refine reasoning trajectories. We first employ MCTS to estimate and select chosen and rejected rollouts, thereby constructing a high-quality step-level dataset. Our PPRM is trained on Bradley-Terry loss function, which mitigates the bias introduced by the heuristic search strategies of MCTS by leveraging preference-based learning. To enable effective RL training with PPRM, we enhance  Group Relative Policy Optimization (GRPO)  by introducing a robust advantage estimator that better captures the structure of preference-based process reward model enabling stable and efficient policy optimization. Experimental results on ProcessBench and best-of-n strategy  demonstrate that our approach achieves  $2$-$3\%$ improvement in intermediate step accuracy compared to existing methods for complex reasoning processes, thereby improving the reasoning accuracy of the policy model across several key reasoning benchmarks.