When Does Learning to Stop Help? A Cost-Aware Study of Early Exits in Reasoning Models
Reasoning models spend test-time compute unevenly across instances, and a growing family of early-exit rules -- confidence thresholds, entropy monitors, answer-stability checks, and learned stoppers -- promises to reclaim the waste. These rules, however, are evaluated under heterogeneous protocols that leave the deployment question unanswered: at a fixed tolerance for losing correct answers, which policy saves more compute, and does the saving survive probe overhead? We answer this question with a controlled study across 18 task-model settings spanning GSM8K, MATH-500, MMLU-Pro, AIME-90, and GPQA on Qwen3 and DeepSeek-R1-distilled models, using LearnStop, a hidden-state-free logistic stop...
| Authors | Zhe Dong, Fang Qin, Manish Shah |
|---|---|
| Primary category | cs.AI |
| Published | 29 June 2026 |
| Last updated at source | 4 July 2026 |
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