[2607.05861] Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization

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Computer Science > Computation and Language

arXiv:2607.05861 (cs)

[Submitted on 7 Jul 2026]

Title:Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization

Authors:Kaishen Wang, Tong Zheng, Xuehao Cui, Ruibo Chen, Tianyi Xiong, Heng Huang<br>View a PDF of the paper titled Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization, by Kaishen Wang and 5 other authors

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Abstract:Large reasoning models (LRMs) improve language model capabilities by generating explicit thinking traces before final answers. In factuality-oriented question answering (QA), such thinking often improves overall performance by helping the model recover relevant knowledge and refine its answers. However, we find that this benefit is not uniform at the instance level: explicit thinking can also overturn correct non-thinking answers and lead to factual drift. We refer to this failure mode as \emph{thinking-induced hallucination}. To explain this phenomenon, we formulate explicit thinking in factuality QA as a thinking residual over the model's direct-answer tendency, which can either recover missing knowledge or introduce unsupported associations. Based on this formulation, we propose MARGO, \underline{\textit{M}}ixed-Mode \underline{\textit{A}}dvantage \underline{\textit{R}}egularization for \underline{\textit{G}}rounded \underline{\textit{O}}ptimization, a reinforcement learning framework that uses non-thinking rollouts as same-model references in advantage estimation. By constructing mixed-mode rollout groups with both thinking and non-thinking trajectories, MARGO evaluates whether explicit thinking adds factual value beyond direct answering, thereby suppressing hallucination-prone thinking while preserving beneficial thinking behaviors. Experiments across multiple factuality-oriented QA benchmarks demonstrate that MARGO improves factual reliability over strong baselines, while evaluations on mathematical benchmarks show that it preserves general reasoning ability.

Comments:<br>19 pages, 3 figures, 8 tables

Subjects:

Computation and Language (cs.CL); Machine Learning (cs.LG)

Cite as:<br>arXiv:2607.05861 [cs.CL]

(or<br>arXiv:2607.05861v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.05861

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arXiv-issued DOI via DataCite

Submission history<br>From: Kaishen Wang [view email]<br>[v1]<br>Tue, 7 Jul 2026 05:34:25 UTC (1,065 KB)

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