[2607.29211] Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning

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arXiv:2607.29211 (cs)

[Submitted on 31 Jul 2026]

Title:Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning

Authors:Xinyan Guan, Jiali Zeng, Chunlei Xin, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Fandong Meng<br>View a PDF of the paper titled Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning, by Xinyan Guan and 7 other authors

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Abstract:Large language models generate computationally expensive yet semantically void reasoning on beyond-capability tasks, creating risks where plausible-sounding but incorrect derivations mislead users. We characterize this \textit{futile reasoning} phenomenon through systematic analysis, revealing universal capability overreach and systematic miscalibration between capability and behavior. The dominant failure mode is specious reasoning, which outputs look superficially valid but contain subtle errors, escalating with task difficulty. To address this, we introduce \textbf{CaRL} (\textbf{Ca}pability-\textbf{a}ligned \textbf{R}einforcement \textbf{L}earning), which aligns model behavior with capability boundaries through reward shaping that incentivizes refusal over futile reasoning and hindsight refusal augmentation that converts failures into refusal supervision. Experiments demonstrate a substantial reduction in futile reasoning while preserving performance across task difficulties, effectively achieving capability-aligned behavior without sacrificing utility. \footnote{this https URL}

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Computation and Language (cs.CL)

Cite as:<br>arXiv:2607.29211 [cs.CL]

(or<br>arXiv:2607.29211v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.29211

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Xinyan Guan [view email]<br>[v1]<br>Fri, 31 Jul 2026 09:30:33 UTC (727 KB)

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