Towards Robust Process Reward Modeling via Noise-aware Learning

• URL: http://arxiv.org/abs/2601.12748v1
• Date: Mon, 19 Jan 2026 06:03:58 GMT
• Title: Towards Robust Process Reward Modeling via Noise-aware Learning
• Authors: Bin Xie, Bingbing Xu, Xueyun Tian, Yilin Chen, Huawei Shen,
• Abstract summary: We propose a two-stage framework to mitigate noisy supervision.<n>In the labeling stage, we introduce a reflection-aware label correction mechanism that uses a large language model (LLM) as a judge.<n>In the training stage, we propose a underlinetextbfIterative underlinetextbfTraining framework that enables the PRM to progressively refine noisy labels.
• Score: 33.1289107681179
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Process Reward Models (PRMs) have achieved strong results in complex reasoning, but are bottlenecked by costly process-level supervision. A widely used alternative, Monte Carlo Estimation (MCE), defines process rewards as the probability that a policy model reaches the correct final answer from a given reasoning step. However, step correctness is an intrinsic property of the reasoning trajectory, and should be invariant to policy choice. Our empirical findings show that MCE producing policy-dependent rewards that induce label noise, including false positives that reward incorrect steps and false negatives that penalize correct ones. To address above challenges, we propose a two-stage framework to mitigate noisy supervision. In the labeling stage, we introduce a reflection-aware label correction mechanism that uses a large language model (LLM) as a judge to detect reflection and self-correction behaviors related to the current reasoning step, thereby suppressing overestimated rewards. In the training stage, we further propose a \underline{\textbf{N}}oise-\underline{\textbf{A}}ware \underline{\textbf{I}}terative \underline{\textbf{T}}raining framework that enables the PRM to progressively refine noisy labels based on its own confidence. Extensive Experiments show that our method substantially improves step-level correctness discrimination, achieving up to a 27\% absolute gain in average F1 over PRMs trained with noisy supervision.

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