Unverified50% confidenceFactExact time
In RoCo-PRM, the teacher is a strong MoE model whose soft labels come from step scores computed from routing statistics rather than output logits; the student is an independent PRM.
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10/22/2026
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UnverifiedThe researchers propose the RoCo-PRM method, which uses a strong MoE model to automatically annotate reasoning trajectories with routing-based step scores, then distills these scores into an independent PRM.75% similarVerifiedProcess Reward Models (PRM) shift toward evaluating the reasoning process rather than the final answer, and have already demonstrated significant advantages in the domain of mathematical reasoning.66% similarUnverifiedSMD introduces a residual surrogate called Pivot, whose role is to separate local corrections from the student's interval-mean prediction: the student is responsible for predicting the average velocity over the entire interval, and Pivot is responsible for modeling the residual fluctuations within the interval.62% similarUnverifiedThe R2S pipeline has three steps: 1) starting from the untrained student model, mine the states where it diverges most from the teacher; 2) run teacher takeovers from these states and filter the takeover segments using reward-free checks (consensus or LLM judging); 3) fine-tune the teacher scorer on the filtered takeover data, then freeze it as the sole scorer for standard OPD.61% similarVerifiedIn offline distillation, training data is generated once by the teacher model and fixed, with the student model learning from a static dataset.60% similar
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