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SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking

Paper · 2026 · Weiyang Huang, Xuefeng Bai, Kehai Chen et al.

Summary

Large Reasoning Models often "overthink" and produce long chains of thought even for simple problems. Existing remedies are either coarse, such as prompting, routing, or token budgeting, or apply one uniform rule to every step, such as early stopping or suppressing reflection. Both kinds can cut away steps that matter. SAT (Stepwise Adaptive Thinking) instead decides how much effort to spend on each step. It models reasoning as a Finite-State Machine with four modes: SLOW, NORMAL, FAST and SKIP. A small Process Reward Model, called the Pilot, scores the difficulty of the previous step using confidence patterns and semantic context. A transition logic with hysteresis then chooses the next state, and control tags inserted into the context steer the model's depth. The approach needs no training of the backbone LRM. Across 9 LRMs and 7 benchmarks in math, science and code, SAT reduces reasoning tokens by up to 40% while generally keeping or improving accuracy. This makes efficient reasoning deployment more practical without sacrificing logical completeness.

Key points

  • Pilot size: the PRM has about 30M parameters, roughly 99% fewer than typical 7B verifiers. SAT keeps CoT-level throughput (29.98 vs. 30.14 tokens/s) and memory (~30GB), and reaches about a 37% end-to-end speedup.
  • Adapts to task difficulty: with Qwen3-14B, FAST mode covers 98.9% of steps on GSM8K but only 50.3% on AIME 2025, where SLOW+SKIP rise to 26.8%. The weaker Qwen3-4B uses deeper modes more often on AIME 2025 (39.5%).
  • Cross-domain generalization: the Pilot was trained only on math, yet its difficulty scores correlate with a sampling-based difficulty proxy on GPQA (Pearson r=0.716) and HumanEval (r=0.571).
  • Robust thresholds: on MATH500 with Qwen3-14B, small changes to τ_fast and τ_slow leave accuracy near 96.6% (±0.6) and token counts in a narrow range (2842–2904).
  • Savings depend on outcome: on AIME 2025 with Qwen3-8B, SAT saves 24.3% of tokens on correctly solved problems but only 5.2% on failed ones, many of which hit the maximum generation length.

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

🎓Harbin Institute of TechnologyHarbin · China158 works · 28,841 citations🏢Huawei TechnologiesShenzhen · China75 works · 12,975 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 Weiyang Huang, Xuefeng Bai, Kehai Chen, Xinyang Chen, Yibin Chen, Weili Guan and Min Zhang. 2026. SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking. 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.2009

@inproceedings{huang2026sat,
  title = {{SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking}},
  author = {Weiyang Huang, Xuefeng Bai, Kehai Chen, Xinyang Chen, Yibin Chen, Weili Guan 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.2009},
  url = {https://doi.org/10.18653/v1/2026.acl-long.2009}
}

References

58 references, 16 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Pranjal Aggarwal and Sean Welleck. 2025. L1: Controlling how long a reasoning model thinks with reinforcement learning. In Second Conference on Language Modeling.
  2. AI-MO. 2024. AMC 2023, 2024. https://huggingface.co/datasets/AI-MO/ aimo-validation-amc. ↗
  3. Akhiad Bercovich, Itay Levy, Izik Golan, Mohammad Dabbah, Ran El-Yaniv, Omri Puny, Ido Galil, Zach Moshe, Tomer Ronen, Najeeb Nabwani, Ido Shahaf, Oren Tropp, Ehud Karpas, Ran Zilberstein, Jiaqi Zeng, Soumye Singhal, Alexander Bukharin, Yian Zhang, Tugrul Konuk, and 114 others. 2025. Llama-nemotron:…
  4. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, and 39 others. 202… — Evaluating Large Language Models Trained on Code
  5. Qiguang Chen, Libo Qin, Jiaqi WANG, Jingxuan Zhou, and Wanxiang Che. 2024. Unlocking the capabilities of thought: A reasoning boundary framework to quantify and optimize chain-of-thought. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. find
  6. Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, and Dong Yu. 2025. Do NOT think that much for 2+3=? on the overthinking of long reasoning models. In Forty-second International Conference… find
  7. Xiaoxue Cheng, Junyi Li, Zhenduo Zhang, Xinyu Tang, Xin Zhao, XinYu KONG, and Zhiqiang Zhang. 2025. Incentivizing dual process thinking for efficient large language model reasoning. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. find
  8. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021. Training verifiers to solve math word problems. Preprint, arXiv:2110.14168.
  9. DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, and 181 others. 2025. Deepseek-r1: Incentivizing reasoning capability in ll…
  10. Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, and Dongmei Zhang. 2024. Everything of thoughts: Defying the law of penrose triangle for thought generation. In Findings of the Association for Computational Linguistics: ACL 2024, pages 1638… find
  11. Yichao Fu, Junda Chen, Siqi Zhu, Zheyu Fu, Zhongdongming Dai, Yonghao Zhuang, Yian Ma, Aurick Qiao, Tajana Rosing, Ion Stoica, and Hao Zhang. 2025. Efficiently scaling LLM reasoning programs with certaindex. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. find
  12. Runquan Gui, Yafu Li, Xiaoye Qu, Ziyan Liu, Yeqiu Cheng, and Yu Cheng. 2026. Faithrl: Learning to reason faithfully through step-level faithfulness maximization. find
  13. Tingxu Han, Zhenting Wang, Chunrong Fang, Shiyu Zhao, Shiqing Ma, and Zhenyu Chen. 2025. Token-budget-aware LLM reasoning. In Findings of the Association for Computational Linguistics: ACL 2025, pages 24842–24855, Vienna, Austria. Association for Computational Linguistics.
  14. Jujie He, Tianwen Wei, Rui Yan, Jiacai Liu, Chaojie Wang, Yimeng Gan, Shiwen Tu, Chris Yuhao Liu, Liang Zeng, Xiaokun Wang, Boyang Wang, Yongcong Li, Fuxiang Zhang, Jiacheng Xu, Bo An, Yang Liu, and Yahui Zhou. 2024. Skywork-o1 open series. find
  15. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. Measuring mathematical problem solving with the MATH dataset. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
  16. Jiameng Huang, Baijiong Lin, Guhao Feng, Jierun Chen, Di He, and Lu Hou. 2026. Efficient reasoning for large reasoning language models via certainty-guided reflection suppression. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). find
  17. Yu Kang, Xianghui Sun, Liangyu Chen, and Wei Zou. 2025. C3ot: generating shorter chain-of-thought without compromising effectiveness. In Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty-Seventh Conference on Innovative Applications of Artificial Intelligence and… find
  18. Xingzuo Li, Kehai Chen, Yunfei Long, Xuefeng Bai, Yong Xu, and Min Zhang. 2025. Generator-assistant stepwise rollback framework for large language model agent. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 17683–17700, Suzhou, China. Association for… find
  19. Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang. 2023. Towards general text embeddings with multi-stage contrastive learning. Preprint, arXiv:2308.03281.
  20. MAA Committees. 2024. AIME Problems and Solutions. https://artofproblemsolving.com/wiki/index.php/AIME_Problems_ and_Solutions. ↗
  21. Guosheng Liang, Longguang Zhong, Ziyi Yang, and Xiaojun Quan. 2025. ThinkSwitcher: When to think hard, when to think fast. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 5185–5201, Suzhou, China. Association for Computational Linguistics. find
  22. MAA Committees. 2025. AIME Problems and Solutions. https://artofproblemsolving.com/wiki/index.php/AIME_Problems_ and_Solutions. ↗
  23. Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2024. Let’s verify step by step. In The Twelfth International Conference on Learning Representations.
  24. Junhong Lin, Xinyue Zeng, Jie Zhu, Song Wang, Julian Shun, Jun Wu, and Dawei Zhou. 2025a. Plan and budget: Effective and efficient test-time scaling on large language model reasoning. Preprint, arXiv:2505.16122. — [arXiv:2505.16122](https://arxiv.org/abs/2505.16122) find
  25. Weizhe Lin, Xing Li, Zhiyuan Yang, Xiaojin Fu, Hui-Ling Zhen, Yaoyuan Wang, Xianzhi Yu, Wulong Liu, Xiaosong Li, and Mingxuan Yuan. 2025b. Trimr: Verifier-based training-free thinking compression for efficient test-time scaling. Preprint, arXiv:2505.17155. — [arXiv:2505.17155](https://arxiv.org/abs/2505.17155) find
  26. Wanlong Liu, Junxiao Xu, Fei Yu, Yukang Lin, Ke Ji, Wenyu Chen, Lifeng Shang, Yasheng Wang, Yan Xu, and Benyou Wang. 2025a. QFFT, question-free fine-tuning for adaptive reasoning. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. find
  27. Xiang Liu, Xuming Hu, Xiaowen Chu, and Eunsol Choi. 2025b. Diffadapt: Difficulty-adaptive reasoning for token-efficient llm inference. Preprint, arXiv:2510.19669. find
  28. Xin Liu and Lu Wang. 2025. Answer convergence as a signal for early stopping in reasoning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 17907–17918, Suzhou, China. Association for Computational Linguistics.
  29. Zhipeng Liu, Xuefeng Bai, Kehai Chen, Xinyang Chen, Xiucheng Li, Yang Xiang, Jin Liu, Hong-Dong Li, Yaowei Wang, Liqiang Nie, and Min Zhang. 2025c. A survey on the feedback mechanism of llm-based ai agents. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence… find
  30. Ruilin Luo, Zhuofan Zheng, Yifan Wang, Yiyao Yu, Xinzhe Ni, Zicheng Lin, Jin Zeng, and Yujiu Yang. 2025. Ursa: Understanding and verifying chain-of-thought reasoning in multimodal mathematics. arXiv preprint arXiv:2501.04686. — [arXiv:2501.04686](https://arxiv.org/abs/2501.04686) find
  31. OpenAI. 2025. Learning to reason with llms. https://openai.com/research/ learning-to-reason-with-llms. ↗
  32. Deng Qiyuan, Xuefeng Bai, Kehai Chen, Yaowei Wang, Liqiang Nie, and Min Zhang. 2025. Efficient safety alignment of large language models via preference reranking and representation-based reward modeling. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volu… find
  33. Xiaoye Qu, Yafu Li, Zhao-Chen Su, Weigao Sun, Jianhao Yan, Dongrui Liu, Ganqu Cui, Daizong Liu, Shuxian Liang, Junxian He, Peng Li, Wei Wei, Jing Shao, Chaochao Lu, Yue Zhang, Xian-Sheng Hua, Bowen Zhou, and Yu Cheng. 2025. A survey of efficient reasoning for large reasoning models: Language, multim…
  34. QwenTeam. 2025. Qwq-32b: Embracing the power of reinforcement learning. find
  35. David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. 2024. GPQA: A graduate-level google-proof q&a benchmark. In First Conference on Language Modeling.
  36. Matthew Renze and Erhan Guven. 2024. The benefits of a concise chain of thought on problem-solving in large language models. In 2024 2nd International Conference on Foundation and Large Language Models (FLLM), pages 476–483. find
  37. Zhiwen Ruan, Yixia Li, He Zhu, Yun Chen, Peng Li, Yang Liu, and Guanhua Chen. 2025a. Enhancing large language model reasoning via selective critical token fine-tuning. find
  38. Zhiwen Ruan, Yixia Li, He Zhu, Longyue Wang, Weihua Luo, Kaifu Zhang, Yun Chen, and Guanhua Chen. 2025b. LayAlign: Enhancing multilingual reasoning in large language models via layer-wise adaptive fusion and alignment strategy. In Findings of the Association for Computational Linguistics: NAACL 2025… find
  39. Swarnadeep Saha, Archiki Prasad, Justin Chen, Peter Hase, Elias Stengel-Eskin, and Mohit Bansal. 2025. System 1.x: Learning to balance fast and slow planning with language models. In The Thirteenth International Conference on Learning Representations. find
  40. Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi, Wenjing Zhang, Jiangze Yan, Ning Wang, Kai Wang, Zhaoxiang Liu, and Shiguo Lian. 2025. DAST: Difficulty-adaptive slow-thinking for large reasoning models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Indust… find
  41. Weiqin Wang, Yile Wang, Kehao Chen, and Hui Huang. 2025d. Beyond majority voting: Towards fine-grained and more reliable reward signal for test-time reinforcement learning. arXiv preprint arXiv:2512.15146.
  42. Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen Zhong, Na Zou, Hanjie Chen, and Xia Hu. 2025. Stop overthinking: A survey on efficient reasoning for large language models. Transactions on Machine Learning Research.
  43. Weiqin Wang, Yile Wang, and Hui Huang. 2025e. Ranked voting based self-consistency of large language models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 14410– 14426, Vienna, Austria. Association for Computational Linguistics. find
  44. Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao Qiu, Ming Yin, Mengdi Wang, Peter L. Bartlett, and Andrea Zanette. 2024. Fast best-of-n decoding via speculative rejection. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2… find
  45. Yi Wang, Junxiao Liu, Shimao Zhang, Jiajun Chen, and Shujian Huang. 2025f. Pats: Process-level adaptive thinking mode switching. Preprint, arXiv:2505.19250. — [arXiv:2505.19250](https://arxiv.org/abs/2505.19250) find
  46. Yuxuan Tong, Xiwen Zhang, Rui Wang, Ruidong Wu, and Junxian He. 2024. DART-math: Difficulty-aware rejection tuning for mathematical problem-solving. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. find
  47. Chenlong Wang, Yuanning Feng, Dongping Chen, Zhaoyang Chu, Ranjay Krishna, and Tianyi Zhou. 2025a. Wait, we don’t need to “wait”! removing thinking tokens improves reasoning efficiency. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 7459–7482, Suzhou, China. Associat… find
  48. Hongru Wang, Deng Cai, Wanjun Zhong, Shijue Huang, Jeff Z. Pan, Zeming Liu, and Kam-Fai Wong. 2025b. Self-reasoning language models: Unfold hidden reasoning chains with few reasoning catalyst. In Findings of the Association for Computational Linguistics: ACL 2025, pages 5578–5596, Vienna, Austria. A… find
  49. Jingyao Wang, Wenwen Qiang, Zeen Song, Changwen Zheng, and Hui Xiong. 2025c. Learning to think: Information-theoretic reinforcement fine-tuning for LLMs. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. find
  50. Long Papers), pages 9426–9439, Bangkok, Thailand. Association for Computational Linguistics. find
  51. Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, and Yiming Yang. 2025. Inference scaling laws: An empirical analysis of compute-optimal inference for LLM problem-solving. In The Thirteenth International Conference on Learning Representations. find
  52. Silei Xu, Wenhao Xie, Lingxiao Zhao, and Pengcheng He. 2025. Chain of draft: Thinking faster by writing less. Preprint, arXiv:2502.18600. find
  53. Jianhao Yan, Yafu Li, Zican Hu, Zhi Wang, Ganqu Cui, Xiaoye Qu, Yu Cheng, and Yue Zhang. Learning to reason under off-policy guidance. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Cha… — Qwen3 Technical Report
  54. Chenxu Yang, Qingyi Si, Yongjie Duan, Zheliang Zhu, Chenyu Zhu, Qiaowei Li, Minghui Chen, Zheng Lin, and Weiping Wang. 2025b. Dynamic early exit in reasoning models. Preprint, arXiv:2504.15895. find
  55. Ke Wang, Jiahui Zhu, Minjie Ren, Zeming Liu, Shiwei Li, Zongye Zhang, Chenkai Zhang, Xiaoyu Wu, Qiqi Zhan, Qingjie Liu, and Yunhong Wang. 2024a. A survey on data synthesis and augmentation for large language models. CoRR, abs/2410.12896. — [arXiv:2410.12896](https://arxiv.org/abs/2410.12896) find
  56. Lei Yang, Shaoyang Xu, Jianxiang Peng, Shaolin Zhu, and Deyi Xiong. 2025c. DCIS: Efficient length extrapolation of LLMs via divide-and-conquer scaling factor search. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 15168–15182, Suzhou, China. Associati… find
  57. Peiyi Wang, Lei Li, Zhihong Shao, Runxin Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, and Zhifang Sui. 2024b. Math-shepherd: Verify and reinforce LLMs step-by-step without human annotations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Zewei Yu,…
  58. Shaolin Zhu, Supryadi, Shaoyang Xu, Haoran Sun, Leiyu Pan, Menglong Cui, Jiangcun Du, Renren Jin, António Branco, and Deyi Xiong. 2024. Multilingual large language models: A systematic survey. CoRR, abs/2411.11072. — [arXiv:2411.11072](https://arxiv.org/abs/2411.11072) find

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