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This paper characterizes 'futile reasoning' in large language models, where models produce superficially valid but incorrect reasoning on tasks beyond their capability. They introduce CaRL, a capability-aligned reinforcement learning method that trains LLMs to abstain from futile reasoning while preserving performance.