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arXiv · 2602.10004

ESTAR: Early-Stopping Token-Aware Reasoning For Efficient Inference

Junda Wang·Zhichao Yang·Dongxu Zhang·Sanjit Singh Batra·Robert E. Tillman
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Abstract

Large reasoning models (LRMs) achieve state-of-the-art performance by generating long chains-of-thought, but often waste computation on redundant reasoning after the correct answer has already been reached. We introduce Early-Stopping for Token-Aware Reasoning (ESTAR), which detects and reduces such reasoning redundancy to improve efficiency without sacrificing accuracy. Our method combines (i) a trajectory-based classifier that identifies when reasoning can be safely stopped, (ii) supervised fine-tuning to teach LRMs to propose self-generated signals, and (iii) -aware reinforcement learning that truncates rollouts at self-generated stop points with compute-aware rewards. Experiments on four reasoning datasets show that ESTAR reduces reasoning length by about 3.7x (from 4,799 to 1,290) while preserving accuracy (74.9% vs. 74.2%), with strong cross-domain generalization. These results highlight early stopping as a simple yet powerful mechanism for improving reasoning efficiency in LRMs.

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Junda Wang, Zhichao Yang, Dongxu Zhang, Sanjit Singh Batra, Robert E. Tillman. 2026-02-10. ESTAR: Early-Stopping Token-Aware Reasoning For Efficient Inference. https://arxiv.org/abs/2602.10004

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