Large language models often generate lengthy, plausible-sounding reasoning that is actually incorrect, especially on tasks beyond their capabilities. This phenomenon, termed 'futile reasoning,' poses risks as users may be misled by superficially valid but flawed derivations.
In a new paper accepted as an ACL 2026 Finding, researchers systematically analyze this behavior, revealing 'universal capability overreach' and a systematic miscalibration between model capability and behavior. The dominant failure mode is 'specious reasoning,' where outputs look valid but contain subtle errors that escalate with task difficulty.
To address this, the team introduces CaRL (Capability-aligned Reinforcement Learning). CaRL uses reward shaping to incentivize refusal over futile reasoning, and 'hindsight refusal augmentation' to convert failures into refusal supervision. This aligns model behavior with capability boundaries.
Experiments show that CaRL substantially reduces futile reasoning while preserving performance across task difficulties, achieving capability-aligned behavior without sacrificing utility. The code is available on GitHub.