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    <abstract>Reasoning prefixes shape the future trajectory of LLM problem solving, yet existing process reward models usually evaluate them through local step correctness. We argue that correctness is a useful but indirect proxy for the effect we ultimately care about: whether a prefix increases the probability of successful completion. We define this effect as prefix gain, the solve-rate improvement induced by conditioning lightweight student model group on a prefix, and use it to train a Prefix Utility Model (PUM) with a simple pairwise ranking objective. PUM learns outcome-grounded prefix utility and can score both complete trajectories and partial reasoning prefixes. Across Best-of-N selection, beam search, and reinforcement learning on mathematical reasoning, PUM provides a strong prefix-level supervision signal, especially when candidate pools are large, search budgets increase, or rule-based rewards are sparse.</abstract>
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%0 Conference Proceedings
%T From Correctness to Utility: Gain-Based Prefix Evaluation for LLM Reasoning
%A Zhou, Yuhang
%A Cao, Yixin
%A Ye, Guangnan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhou-etal-2026-correctness
%X Reasoning prefixes shape the future trajectory of LLM problem solving, yet existing process reward models usually evaluate them through local step correctness. We argue that correctness is a useful but indirect proxy for the effect we ultimately care about: whether a prefix increases the probability of successful completion. We define this effect as prefix gain, the solve-rate improvement induced by conditioning lightweight student model group on a prefix, and use it to train a Prefix Utility Model (PUM) with a simple pairwise ranking objective. PUM learns outcome-grounded prefix utility and can score both complete trajectories and partial reasoning prefixes. Across Best-of-N selection, beam search, and reinforcement learning on mathematical reasoning, PUM provides a strong prefix-level supervision signal, especially when candidate pools are large, search budgets increase, or rule-based rewards are sparse.
%U https://aclanthology.org/2026.findings-emnlp.861/
%P 18212-18231

Markdown (Informal)

[From Correctness to Utility: Gain-Based Prefix Evaluation for LLM Reasoning](https://aclanthology.org/2026.findings-emnlp.861/) (Zhou et al., Findings 2026)

• From Correctness to Utility: Gain-Based Prefix Evaluation for LLM Reasoning (Zhou et al., Findings 2026)

ACL

• Yuhang Zhou, Yixin Cao, and Guangnan Ye. 2026. From Correctness to Utility: Gain-Based Prefix Evaluation for LLM Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 18212–18231, Budapest, Hungary. Association for Computational Linguistics.