Unsupervised Process Reward Models

• URL: http://arxiv.org/abs/2605.10158v1
• Date: Mon, 11 May 2026 08:05:27 GMT
• Title: Unsupervised Process Reward Models
• Abstract summary: Process Reward Models (PRMs) are powerful mechanisms for steering large language model reasoning.<n>PRMs require expert annotations for every reasoning step, making them costly and difficult to scale.<n>Here, we propose a method for training unsupervised PRMs that requires no human supervision, neither at the level of step-by-step annotations nor through ground-truth verification of final answers.
• Score: 22.283171054858496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Process Reward Models (PRMs) are a powerful mechanism for steering large language model reasoning by providing fine-grained, step-level supervision. However, this effectiveness comes at a significant cost: PRMs require expert annotations for every reasoning step, making them costly and difficult to scale. Here, we propose a method for training unsupervised PRMs (uPRM) that requires no human supervision, neither at the level of step-by-step annotations nor through ground-truth verification of final answers. The key idea behind our approach is to define a scoring function, derived from LLM next-token probabilities, that jointly assesses candidate positions of first erroneous steps across a batch of reasoning trajectories. We demonstrate the effectiveness of uPRM across diverse scenarios: (i) uPRM achieves up to 15% absolute accuracy improvements over the LLM-as-a-Judge in identifying first erroneous steps on the ProcessBench dataset; (ii) as a verifier for test-time scaling, uPRM performs comparably to supervised PRMs and outperforms the majority voting baseline by up to 6.9%, and (iii) when used as a reward signal in reinforcement learning, uPRM enables more robust policy optimization throughout training compared to a supervised PRM trained using ground-truth labels. Overall, our results open a path toward scalable reward modeling for complex reasoning tasks.

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