Process Rewards with Learned Reliability

• URL: http://arxiv.org/abs/2605.15529v1
• Date: Fri, 15 May 2026 01:57:11 GMT
• Title: Process Rewards with Learned Reliability
• Abstract summary: Downstream methods must treat imperfect step-level reward predictions as reliable decision signals.<n>We propose BetaPRM, a distributional PRM that predicts both a step-level success probability and the reliability of that prediction.<n>We show that BetaPRM improves PRM-guided Best-of-N selection while preserving standard step-level error detection.
• Score: 25.86885718567082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Process Reward Models (PRMs) provide step-level feedback for reasoning, but current PRMs usually output only a single reward score for each step. Downstream methods must therefore treat imperfect step-level reward predictions as reliable decision signals, with no indication of when these predictions should be trusted. We propose BetaPRM, a distributional PRM that predicts both a step-level success probability and the reliability of that prediction. Given step-success supervision from Monte Carlo continuations, BetaPRM learns a Beta belief that explains the observed number of successful continuations through a Beta-Binomial likelihood, rather than regressing to the finite-sample success ratio as a point target. This learned reliability signal indicates when a step reward should be trusted, enabling downstream applications to distinguish reliable rewards from uncertain ones. As one application, we introduce Adaptive Computation Allocation (ACA) for PRM-guided Best-of-N reasoning. ACA uses the learned reliability signal to stop when a high-reward solution is reliable and to spend additional computation on uncertain candidate prefixes. Experiments across four backbones and four reasoning benchmarks show that BetaPRM improves PRM-guided Best-of-N selection while preserving standard step-level error detection. Built on this signal, ACA improves the accuracy--token tradeoff over fixed-budget Best-of-16, reducing token usage by up to 33.57% while improving final-answer accuracy.

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