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arXiv · 2605.05893

Logic-Regularized Verifier Elicits Reasoning from LLMs

Xinyu Wang·Changzhi Sun·Lian Cheng·Yuanbin Wu·Dell Zhang·Xiaoling Wang·Xuelong Li
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Abstract

Verifiers are crucial components for enhancing modern LLMs' reasoning capability. Typicalverifiers require resource-intensive superviseddataset construction, which is costly and faceslimitations in data diversity. In this paper, wepropose LOVER, an unsupervised verifier regularized by logical rules. LOVER treats theverifier as a binary latent variable, utilizinginternal activations and enforcing three logical constraints on multiple reasoning paths:negation consistency, intra-group consistency,and inter-group consistency (grouped by thefinal answer). By incorporating logical rulesas priors, LOVER can leverage unlabeled examples and is directly compatible with any offthe-shelf LLMs. Experiments on 10 datasetsdemonstrate that LOVER significantly outperforms unsupervised baselines, achieving performance comparable to the supervised verifier(reaching its 95% level on average). The sourcecode is publicly available at https://github.com/wangxinyufighting/llm-lover.

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Xinyu Wang, Changzhi Sun, Lian Cheng, Yuanbin Wu, Dell Zhang, Xiaoling Wang, Xuelong Li. 2026-05-07. Logic-Regularized Verifier Elicits Reasoning from LLMs. https://doi.org/10.18653/v1/2025.acl-long.1567

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