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

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TL;DR - An arXiv cs.CL paper characterizing "futile reasoning" — LLMs burning compute on beyond-capability tasks and producing plausible-but-wrong derivations — and proposing CaRL, a reinforcement learning method that teaches models to refuse instead. It matters because confident specious reasoning misleads users and wastes inference budget.

  • Systematic analysis finds universal capability overreach and miscalibration between what models can do and how they behave; the dominant failure mode is specious reasoning (superficially valid output with subtle errors), which worsens as task difficulty rises.
  • CaRL (Capability-aligned Reinforcement Learning) uses reward shaping that incentivizes refusal over futile reasoning.
  • It adds hindsight refusal augmentation, converting failed attempts into refusal supervision signal.
  • Reported experiments show substantially less futile reasoning while preserving task performance across difficulty levels; code is linked at github.com/icip-cas/Knowing-When-to-Quit. No specific benchmark numbers are given in the abstract.

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

arXiv cs.CL Xinyan Guan, Jiali Zeng, Chunlei Xin, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Fandong Meng 2026-07-31 arXiv:2607.29211
Public signals Hugging Face upvotes 14
Providers: Hugging Face · Upvotes 14 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-31 14:29:39.773282 UTC

TL;DR - An arXiv cs.CL paper characterizing "futile reasoning" — LLMs burning compute on beyond-capability tasks and producing plausible-but-wrong derivations — and proposing CaRL, a reinforcement learning method that teaches models to refuse instead. It matters because confident specious reasoning misleads users and wastes inference budget.

  • Systematic analysis finds universal capability overreach and miscalibration between what models can do and how they behave; the dominant failure mode is specious reasoning (superficially valid output with subtle errors), which worsens as task difficulty rises.
  • CaRL (Capability-aligned Reinforcement Learning) uses reward shaping that incentivizes refusal over futile reasoning.
  • It adds hindsight refusal augmentation, converting failed attempts into refusal supervision signal.
  • Reported experiments show substantially less futile reasoning while preserving task performance across difficulty levels; code is linked at github.com/icip-cas/Knowing-When-to-Quit. No specific benchmark numbers are given in the abstract.
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