StepWiser: Stepwise Generative Judges for Wiser Reasoning

• URL: http://arxiv.org/abs/2508.19229v2
• Date: Wed, 27 Aug 2025 17:17:02 GMT
• Title: StepWiser: Stepwise Generative Judges for Wiser Reasoning
• Authors: Wei Xiong, Wenting Zhao, Weizhe Yuan, Olga Golovneva, Tong Zhang, Jason Weston, Sainbayar Sukhbaatar,
• Abstract summary: Process reward models address this by providing step-by-step feedback.<n>Inspired by recent advances, we reframe stepwise reward modeling from a classification task to a reasoning task itself.<n>We show it provides (i) better judgment accuracy on intermediate steps than existing methods; (ii) can be used to improve the policy model at training time; and (iii) improves inference-time search.
• Score: 52.32416311990343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As models increasingly leverage multi-step reasoning strategies to solve complex problems, supervising the logical validity of these intermediate steps has become a critical research challenge. Process reward models address this by providing step-by-step feedback, but current approaches have two major drawbacks: they typically function as classifiers without providing explanations, and their reliance on supervised fine-tuning with static datasets limits generalization. Inspired by recent advances, we reframe stepwise reward modeling from a classification task to a reasoning task itself. We thus propose a generative judge that reasons about the policy model's reasoning steps (i.e., meta-reasons), outputting thinking tokens before delivering a final verdict. Our model, StepWiser, is trained by reinforcement learning using relative outcomes of rollouts. We show it provides (i) better judgment accuracy on intermediate steps than existing methods; (ii) can be used to improve the policy model at training time; and (iii) improves inference-time search.

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