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.
@article{arxiv.2508.19229,
title = {StepWiser: Stepwise Generative Judges for Wiser Reasoning},
author = {Wei Xiong and Wenting Zhao and Weizhe Yuan and Olga Golovneva and Tong Zhang and Jason Weston and Sainbayar Sukhbaatar},
journal= {arXiv preprint arXiv:2508.19229},
year = {2025}
}