Math-Shepherd replaces 800K human labels with rollouts, lifting GSM8K from 77.9% to 84.1%
le_james94 · x · 2026-09-16
In the context of verification becoming the job: process reward models score every reasoning step, and training one took 800,000 human labels. Math-Shepherd swaps the annotator for rollouts — a step is good if the model can still reach the right answer from it — improving GSM8K from 77.9% to 84.1%.
Related event: Three Weeks with Stanford CS329A: Generators Have Outrun Verifiers(9 posts)→
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