Paper proposes early stopping for large reasoning models using confidence dynamics to improve inference efficiency. The technique monitors model confidence during generation and terminates when confidence drops below a threshold, reducing computational costs while maintaining output quality.
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Early Stopping for Large Reasoning Models via Confidence Dynamics
Confidence-based early stopping reduces inference costs for large reasoning models without sacrificing output quality by terminating generation when model certainty drops below a threshold.
Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline
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