Researchers Propose Prefix Utility Model to Improve LLM Reasoning Evaluation Beyond Correctness
A team of researchers has introduced a new framework called Prefix Utility Model (PUM) that evaluates intermediate reasoning steps in large language models by measuring whether those steps actually improve the likelihood of reaching a correct final answer. Unlike existing process reward models that assess step-by-step correctness in isolation, PUM uses a metric called 'prefix gain' — the improvement in solve-rate when a model is conditioned on a given reasoning prefix. The approach offers a more outcome-grounded supervision signal for mathematical reasoning tasks, particularly when search budgets are large or rule-based rewards are sparse.
Current methods for evaluating reasoning in large language models (LLMs) typically rely on process reward models that judge each reasoning step by its local correctness, which the authors argue is only an indirect proxy for what truly matters: whether a given reasoning prefix increases the probability of successfully completing a problem. To address this, the researchers define 'prefix gain' as the improvement in solve-rate induced by conditioning a lightweight student model on a particular prefix, and use this signal to train a Prefix Utility Model (PUM) via a pairwise ranking objective. PUM is designed to score both complete reasoning trajectories and partial prefixes, making it applicable across multiple search and training paradigms. Experiments on mathematical reasoning benchmarks show PUM provides strong performance gains in Best-of-N selection, beam search, and reinforcement learning settings. The benefits are most pronounced when candidate pools are large, search budgets increase, or sparse rule-based rewards limit the usefulness of traditional signals. The authors have released all associated data, models, and code publicly. The work was submitted to arXiv on June 5, 2026.
What different sources said
- arXiv cs.CLCenter
From Correctness to Utility: Gain-Based Prefix Evaluation for LLM Reasoning
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