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ScalePRM: Training Process Reward Models by Scaling Verification Compute Without Ground Truth
Training process reward models (PRMs) requires step-level correctness labels, obtained either through expensive human annotation or by relying on ground-truth answers, limiting the ability to scale process-level supervision. We propose ScalePRM, which scales verification compute as an alternative: g
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