Process Rewards with Learned Reliability
The paper introduces BetaPRM, a distributional Process Reward Model that predicts both success probability and reliability of predictions. This model allows downstream applications to differentiate between reliable and uncertain rewards, enhancing decision-making processes. Additionally, the Adaptive Computation Allocation method utilizes this reliability signal to optimize computation resources effectively.
- ▪BetaPRM predicts both a step-level success probability and the reliability of that prediction.
- ▪The model improves PRM-guided Best-of-N selection while maintaining standard step-level error detection.
- ▪Adaptive Computation Allocation uses the learned reliability signal to optimize computation usage.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15529 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Computation and Language arXiv:2605.15529 (cs) [Submitted on 15 May 2026] Title:Process Rewards with Learned Reliability Authors:Jinyuan Li, Langlin Huang, Chengsong Huang, Shaoyang Xu, Donghong Cai, Yuyi Yang, Wenxuan Zhang, Jiaxin Huang View a PDF of the paper titled Process Rewards with Learned Reliability, by Jinyuan Li and 7 other authors View PDF HTML (experimental) 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.
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