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arXiv · 2610.29498

A Strong MoE Is All You Need for Process Supervision

Guanzhi Deng·Zengyan Liu·Haibo Wang·Kuan Wu·Shing Yin Wong·Sichun Luo·Zhuoran Wang·Linqi Song
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Abstract

Process reward models (PRMs) provide fine-grained supervision for multi-step reasoning, yet obtaining reliable process labels remains a central bottleneck. Existing approaches typically rely on human annotation, rollout-based estimation, or auxiliary verification. In this work, we explore a different source of process supervision natively exposed by Mixture-of-Experts (MoE) models: expert routing. We show that simple routing statistics provide an intrinsic process-confidence signal that is competitive with conventional output-logit confidence. Building on this observation, we construct routing confidence by combining overall routing mass, head routing mass, and within-step routing variation, and introduce RoCo-Rethink, which identifies the least confident reasoning step and selectively regenerates the subsequent trajectory. We further introduce RoCo-PRM, which uses a strong MoE to automatically annotate reasoning trajectories with routing-derived step scores and distills them into a standalone PRM. Across multiple MoE architectures and diverse reasoning tasks, both RoCo-Rethink and RoCo-PRM consistently outperform strong baselines, while RoCo-PRM transfers effectively across both model architectures and tasks. Our findings suggest that expert routing is more than a computational dispatch mechanism: it provides an architecture-native source of process supervision for reasoning.

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Guanzhi Deng, Zengyan Liu, Haibo Wang, Kuan Wu, Shing Yin Wong, Sichun Luo, Zhuoran Wang, Linqi Song. 2026-10-21. A Strong MoE Is All You Need for Process Supervision. https://arxiv.org/abs/2610.29498

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