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When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning

Paper · 2026 · Yang Xiang, Yixin Ji, Ruotao Xu et al.

Summary

Large reasoning models tend to overthink: they keep re-verifying or trying other strategies after they already have a correct solution, which wastes tokens. Earlier early-exit methods (Dynasor-CoT, DEER, CGRS) probe intermediate answers and judge them by consistency or entropy. This is unreliable because models can be overconfident when wrong, and it only works when answers are short and have a fixed format. The authors propose DTSR (Dynamic Thought Sufficiency in Reasoning), a training-free decoding method modelled on human metacognition. During generation, the model watches for reflection cues such as "Wait". At each cue, it rates whether its chain-of-thought so far is enough to answer the question. If the score passes a threshold, the thinking phase is closed and the final answer is produced. Because DTSR needs no intermediate answer, it also applies to code and open-ended questions. On Qwen3 models, it cuts reasoning length by 28.9%–34.9% with minimal accuracy loss and beats the baselines. This makes inference cheaper without any retraining.

Key points

  • Two stages. (1) Reflection Signal Monitoring detects cue tokens such as "Wait", "Alternatively" and "But…" phrases. (2) Thought Sufficiency Check prompts the model to give a scalar sufficiency score for the current CoT. If the score exceeds threshold τ, </think> is appended; otherwise reasoning resumes.
  • Signal set origin. The reflection signals come from an analysis of Qwen3-32B trajectories. Earliest-correct exit points were often followed by verification-style cues.
  • Check spacing. A minimum token interval k between checks avoids repeated, redundant evaluations when cues appear close together.
  • Results. Experiments used Qwen3 models of several sizes, up to 32B, on mathematics, programming and open-ended questions. Reasoning length dropped by 28.9%–34.9% with near-lossless performance.
  • Analysis and limits. The paper also discusses LRM overconfidence and the self-evaluation paradigm, in which the model judges its own trace from a third-person view. Stated limitations are that no models above 32B were tested and that the work covers text-only tasks, with no multimodal or agent settings.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Soochow UniversitySuzhou · China63 works · 4,507 citations🏢ByteDanceBeijing · China41 works · 4,180 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Yang Xiang, Yixin Ji, Ruotao Xu, Dan Qiao, Zheming Yang, Juntao Li and Min Zhang. 2026. When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1080

@inproceedings{xiang2026when,
  title = {{When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning}},
  author = {Yang Xiang, Yixin Ji, Ruotao Xu, Dan Qiao, Zheming Yang, Juntao Li and Min Zhang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1080},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1080}
}

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