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Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2503.16419

Summary

Reasoning-focused LLMs such as OpenAI o1 and DeepSeek-R1 get better at math and coding by writing long Chain-of-Thought traces. They often use far more tokens than a task needs, though. Even a trivial sum can produce thousands of tokens of deliberation. This "overthinking" raises inference cost and latency, which makes these models hard to use where response time matters. Researchers from Rice University and the University of Houston present what they call the first structured survey of ways to shorten reasoning without losing accuracy. They group the literature into three families. Model-based methods change the model itself, through RL with length-aware rewards or SFT on CoT data of varying length. Output-based methods change what the model generates, by packing steps into latent representations or adapting decoding at inference time. Prompt-based methods change the input, through budget-setting prompts or routing queries by difficulty. Further chapters cover data-efficient training, small models and compression, and evaluation. The paper runs no new experiments. Its value is a clear map of a fast-growing area and a GitHub list of papers that the authors keep updating.

Key points

  • The taxonomy has six core categories: RL length rewards, variable-length CoT SFT, latent compression, dynamic inference, prompt-guided reasoning and routing. It also covers efficient data, small LMs and benchmarking.
  • Representative works by category:
    • RL with length rewards: Kimi k1.5, O1-Pruner, L1, ThinkPrune
    • SFT on variable-length CoT: TokenSkip, CoT-Valve, C3oT
    • Latent compression: Coconut, CODI, SoftCoT
    • Prompt-guided reasoning: Chain of Draft, Token-Budget, NoThinking
    • Routing: RouteLLM, Claude 3.7 Sonnet
  • Data and evaluation: LIMO, s1 and Light-R1 train reasoning models on small curated datasets. Sys2Bench and S1-Bench are among the benchmarks for measuring efficiency.
  • The authors separate efficient reasoning from model compression such as quantization or KV cache compression. Efficient reasoning shortens the generated output rather than shrinking the model.
  • Open issues they discuss:
    • RL vs. SFT: they suggest combining the two.
    • Safety vs. efficiency: for example, the H-CoT attack lowers refusal rates.
    • Efficient reasoning for agentic AI.
  • The resource is the repository Awesome-Efficient-Reasoning-LLMs. The paper is arXiv v4, August 2025.

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

Fields: Natural language processing · AI & machine learning

Cited by

150 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 149 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2503.16419 · arXiv 2503.16419

@misc{sui2025stop,
  title = {{Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models}},
  author = {Sui, Yang and Chuang, Yu-Neng and Wang, Guanchu and Zhang, Jiamu and Zhang, Tianyi and Yuan, Jiayi and Liu, Hongyi and Wen, Andrew and Zhong, Shaochen and Zou, Na and Chen, Hanjie and Hu, Xia},
  year = {2025},
  doi = {10.48550/arxiv.2503.16419},
  eprint = {2503.16419},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2503.16419}
}

References

250 references, 18 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Aradhye Agarwal, Ayan Sengupta, and Tanmoy Chakraborty. First finish search: Efficient test-time scaling in large language models, 2025. 4 find
  2. Pranjal Aggarwal and Sean Welleck. L1: Controlling how long a reasoning model thinks with reinforcement learning. arXiv preprint arXiv:2503.04697, 2025. 4, 6, 7 find our card
  3. Sohyun An, Ruochen Wang, Tianyi Zhou, and Cho-Jui Hsieh. Don’t think longer, think wisely: Optimizing thinking dynamics for large reasoning models, 2025. 4 find
  4. Anthropic. Claude 3.7 sonnet, 2023. Accessed: March 10, 2025. 4, 15 find
  5. Daman Arora and Andrea Zanette. Training language models to reason efficiently. arXiv preprint arXiv:2502.04463, 2025. 4, 6 find
  6. Simon A Aytes, Jinheon Baek, and Sung Ju Hwang. Sketch-of-thought: Efficient llm reasoning with adaptive cognitive-inspired sketching. arXiv preprint arXiv:2503.05179, 2025. 4, 15 find
  7. Seyedarmin Azizi, Erfan Baghaei Potraghloo, and Massoud Pedram. Activation steering for chain-of-thought compression, 2025. 4 find
  8. Edward Beeching, Lewis Tunstall, and Sasha Rush. Scaling test-time compute with open models. 11 find
  9. Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. Graph of thoughts: Solving elaborate problems with large language models. In Proceedings of the AAAI Conference on Artificial Int… find
  10. Mert Cemri, Nived Rajaraman, Rishabh Tiwari, Xiaoxuan Liu, Kurt Keutzer, Ion Stoica, Kannan Ramchandran, Ahmad Beirami, and Ziteng Sun. SPECS: Faster test-time scaling through speculative drafts, 2025. 4 find
  11. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021. 1 find our card
  12. Qiguang Chen, Dengyun Peng, Jinhao Liu, HuiKang Su, Jiannan Guan, Libo Qin, and Wanxiang Che. Aware first, think less: Dynamic boundary self-awareness drives extreme reasoning efficiency in large language models, 2025. 4 find
  13. Qiguang Chen, Libo Qin, Jiaqi Wang, Jingxuan Zhou, and Wanxiang Che. Unlocking the capabilities of thought: A reasoning boundary framework to quantify and optimize chain-of-thought. Advances in Neural Information Processing Systems, 37:54872–54904, 2024. 4, 15 find
  14. Runjin Chen, Zhenyu Zhang, Junyuan Hong, Souvik Kundu, and Zhangyang Wang. Seal: Steerable reasoning calibration of large language models for free. arXiv preprint arXiv:2504.07986, 2025. 4 — [arXiv:2504.07986](https://arxiv.org/abs/2504.07986) find
  15. Xiaoshu Chen, Sihang Zhou, Ke Liang, and Xinwang Liu. Distilling reasoning ability from large language models with adaptive thinking. arXiv preprint arXiv:2404.09170, 2024. 4, 17 — [arXiv:2404.09170](https://arxiv.org/abs/2404.09170) find
  16. Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, et al. Do not think that much for 2+ 3=? on the overthinking of o1-like llms. arXiv preprint arXiv:2412.21187, 2024. 2, 5 find our card
  17. Yilong Chen, Junyuan Shang, Zhenyu Zhang, Yanxi Xie, Jiawei Sheng, Tingwen Liu, Shuohuan Wang, Yu Sun, Hua Wu, and Haifeng Wang. Inner thinking transformer: Leveraging dynamic depth scaling to foster adaptive internal thinking. arXiv preprint arXiv:2502.13842, 2025. 19 find
  18. Zhuokun Chen, Zeren Chen, Jiahao He, Mingkui Tan, Jianfei Cai, and Bohan Zhuang. R-stitch: Dynamic trajectory stitching for efficient reasoning, 2025. 4 find
  19. Zigeng Chen, Xinyin Ma, Gongfan Fang, Ruonan Yu, and Xinchao Wang. Verithinker: Learning to verify makes reasoning model efficient. arXiv preprint arXiv:2505.17941, 2025. 4 — [arXiv:2505.17941](https://arxiv.org/abs/2505.17941) find
  20. Jeffrey Cheng and Benjamin Van Durme. Compressed chain of thought: Efficient reasoning through dense representations. arXiv preprint arXiv:2412.13171, 2024. 4, 10 find
  21. Xiaoxue Cheng, Junyi Li, Zhenduo Zhang, Xinyu Tang, Wayne Xin Zhao, Xinyu Kong, and Zhiqiang Zhang. Incentivizing dual process thinking for efficient large language model reasoning. arXiv preprint arXiv:2505.16315, 2025. 4 — [arXiv:2505.16315](https://arxiv.org/abs/2505.16315) find
  22. Zhengxiang Cheng, Dongping Chen, Mingyang Fu, and Tianyi Zhou. Optimizing length compression in large reasoning models, 2025. 4 find
  23. Li Chenglin, Qianglong Chen, Liangyue Li, Caiyu Wang, Feng Tao, Yicheng Li, Zulong Chen, and Yin Zhang. Mixed distillation helps smaller language models reason better. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 1673–1690, 2024. 4, 17 find
  24. Yu-Neng Chuang, Leisheng Yu, Guanchu Wang, Lizhe Zhang, Zirui Liu, Xuanting Cai, Yang Sui, Vladimir Braverman, and Xia Hu. Confident or seek stronger: Exploring uncertainty-based on-device llm routing from benchmarking to generalization, 2025. 4, 15 find
  25. Yu-Neng Chuang, Helen Zhou, Prathusha Kameswara Sarma, Parikshit Gopalan, John Boccio, Sara Bolouki, and Xia Hu. Learning to route llms with confidence tokens, 2025. 4, 15 find
  26. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. 1 find our card
  27. Codeforces. Codeforces - competitive programming platform, 2025. Accessed: 2025-03-18. 1 find
  28. Rémi Coulom. Efficient selectivity and backup operators in monte-carlo tree search. In International conference on computers and games, pages 72–83. Springer, 2006. 5 find
  29. Alejandro Cuadron, Dacheng Li, Wenjie Ma, Xingyao Wang, Yichuan Wang, Siyuan Zhuang, Shu Liu, Luis Gaspar Schroeder, Tian Xia, Huanzhi Mao, Nicholas Thumiger, Aditya Desai, Ion Stoica, Ana Klimovic, Graham Neubig, and Joseph E. Gonzalez. The danger of overthinking: Examining the reasoning-action dil… find
  30. Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, et al. A survey on multimodal large language models for autonomous driving. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 958–979, 2024… find
  31. Yingqian Cui, Pengfei He, Jingying Zeng, Hui Liu, Xianfeng Tang, Zhenwei Dai, Yan Han, Chen Luo, Jing Huang, Zhen Li, et al. Stepwise perplexity-guided refinement for efficient chain-of-thought reasoning in large language models. arXiv preprint arXiv:2502.13260, 2025. 4 find
  32. Muzhi Dai, Shixuan Liu, and Qingyi Si. Stable reinforcement learning for efficient reasoning, 2025. 4 find
  33. Muzhi Dai, Chenxu Yang, and Qingyi Si. S-grpo: Early exit via reinforcement learning in reasoning models. arXiv preprint arXiv:2505.07686, 2025. 4 — [arXiv:2505.07686](https://arxiv.org/abs/2505.07686) find
  34. Yuntian Deng, Yejin Choi, and Stuart Shieber. From explicit cot to implicit cot: Learning to internalize cot step by step. arXiv preprint arXiv:2405.14838, 2024. 9 find
  35. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volum… find
  36. Bowen Ding, Yuhan Chen, Futing Wang, Lingfeng Ming, and Tao Lin. Do thinking tokens help or trap? towards more efficient large reasoning model, 2025. 4 find
  37. Dujian Ding, Ankur Mallick, Shaokun Zhang, Chi Wang, Daniel Madrigal, Mirian Del Carmen Hipolito Garcia, Menglin Xia, Laks V. S. Lakshmanan, Qingyun Wu, and Victor Rühle. Best-route: Adaptive llm routing with test-time optimal compute, 2025. 4 find
  38. Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, et al. Dynamic parallel tree search for efficient llm reasoning. arXiv preprint arXiv:2502.16235, 2025. 4, 11, 12 find
  39. Jiafei Duan, Samson Yu, Hui Li Tan, Hongyuan Zhu, and Cheston Tan. A survey of embodied ai: From simulators to research tasks. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(2):230–244, 2022. 18 find
  40. Razvan-Gabriel Dumitru, Darius Peteleaza, Vikas Yadav, and Liangming Pan. Conciserl: Conciseness-guided reinforcement learning for efficient reasoning models. arXiv preprint arXiv:2505.17250, 2025. 4 find
  41. Roy Eisenstadt, Itamar Zimerman, and Lior Wolf. Overclocking llm reasoning: Monitoring and controlling thinking path lengths in llms, 2025. 4 find
  42. Sugyeong Eo, Hyeonseok Moon, Evelyn Hayoon Zi, Chanjun Park, and Heuiseok Lim. Debate only when necessary: Adaptive multiagent collaboration for efficient llm reasoning, 2025. 20 find
  43. Chenrui Fan, Ming Li, Lichao Sun, and Tianyi Zhou. Missing premise exacerbates overthinking: Are reasoning models losing critical thinking skill? arXiv preprint arXiv:2504.06514, 2025. 18 — [arXiv:2504.06514](https://arxiv.org/abs/2504.06514) find
  44. Siqi Fan, Peng Han, Shuo Shang, Yequan Wang, and Aixin Sun. Cothink: Token-efficient reasoning via instruct models guiding reasoning models, 2025. 4 find
  45. Gongfan Fang, Xinyin Ma, and Xinchao Wang. Thinkless: Llm learns when to think, 2025. 4 find
  46. Junfeng Fang, Yukai Wang, Ruipeng Wang, Zijun Yao, Kun Wang, An Zhang, Xiang Wang, and Tat-Seng Chua. Safemlrm: Demystifying safety in multi-modal large reasoning models. arXiv preprint arXiv:2504.08813, 2025. 20 — [arXiv:2504.08813](https://arxiv.org/abs/2504.08813) find
  47. Mehdi Fatemi, Banafsheh Rafiee, Mingjie Tang, and Kartik Talamadupula. Concise reasoning via reinforcement learning. arXiv preprint arXiv:2504.05185, 2025. 4 — [arXiv:2504.05185](https://arxiv.org/abs/2504.05185) find
  48. Tao Feng, Yicheng Li, Li Chenglin, Hao Chen, Fei Yu, and Yin Zhang. Teaching small language models reasoning through counterfactual distillation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 5831–5842, 2024. 4, 17 find
  49. Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. Gptq: Accurate post-training quantization for generative pre-trained transformers. In The Eleventh International Conference on Learning Representations. OpenReview, 2023. 2 find
  50. Yichao Fu, Junda Chen, Siqi Zhu, Zheyu Fu, Zhongdongming Dai, Aurick Qiao, and Hao Zhang. Efficiently serving llm reasoning programs with certaindex. arXiv preprint arXiv:2412.20993, 2024. 4, 11, 12 — [arXiv:2412.20993](https://arxiv.org/abs/2412.20993) find
  51. Yichao Fu, Junda Chen, Yonghao Zhuang, Zheyu Fu, Ion Stoica, and Hao Zhang. Reasoning without self-doubt: More efficient chain-of-thought through certainty probing. In ICLR 2025 Workshop on Foundation Models in the Wild, 2025. 4, 13 find
  52. Jiaxuan Gao, Shu Yan, Qixin Tan, Lu Yang, Shusheng Xu, Wei Fu, Zhiyu Mei, Kaifeng Lyu, and Yi Wu. How far are we from optimal reasoning efficiency?, 2025. 4 find
  53. Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, and Tom Goldstein. Scaling up test-time compute with latent reasoning: A recurrent depth approach. arXiv preprint arXiv:2502.05171, 2025. 9 find
  54. Amirhosein Ghasemabadi, Keith G. Mills, Baochun Li, and Di Niu. Guided by gut: Efficient test-time scaling with reinforced intrinsic confidence, 2025. 4 find
  55. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. 1 find
  56. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. 1, 2, 4, 5, 6, 7, 11 find our card
  57. Hasan Abed Al Kader Hammoud, Kumail Alhamoud, Abed Hammoud, Elie Bou-Zeid, Marzyeh Ghassemi, and Bernard Ghanem. Train long, think short: Curriculum learning for efficient reasoning, 2025. 4 find
  58. Tingxu Han, Chunrong Fang, Shiyu Zhao, Shiqing Ma, Zhenyu Chen, and Zhenting Wang. Token-budget-aware llm reasoning. arXiv preprint arXiv:2412.18547, 2024. 2, 4, 8, 9, 14, 15 find our card
  59. Jitai Hao, Yuke Zhu, Tian Wang, Jun Yu, Xin Xin, Bo Zheng, Zhaochun Ren, and Sheng Guo. Omnikv: Dynamic context selection for efficient long-context llms. In The Thirteenth International Conference on Learning Representations, 2025. 2 find
  60. Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian. Training large language models to reason in a continuous latent space. arXiv preprint arXiv:2412.06769, 2024. 2, 4, 10 find
  61. Michael Hassid, Gabriel Synnaeve, Yossi Adi, and Roy Schwartz. Don’t overthink it. preferring shorter thinking chains for improved llm reasoning, 2025. 4 find
  62. Kai He, Rui Mao, Qika Lin, Yucheng Ruan, Xiang Lan, Mengling Feng, and Erik Cambria. A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics. arXiv preprint arXiv:2310.05694, 2023. 19 find
  63. Xingyang He, Xiao Ling, and Jie Liu. Smartthinker: Learning to compress and preserve reasoning by step-level length control, 2025. 4 find
  64. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2021. 1 find our card
  65. Jialiang Hong, Taihang Zhen, Kai Chen, Jiaheng Liu, Wenpeng Zhu, Jing Huo, Yang Gao, Depeng Wang, Haitao Wan, Xi Yang, Boyan Wang, and Fanyu Meng. Reconsidering overthinking: Penalizing internal and external redundancy in cot reasoning, 2025. 4 find
  66. Bairu Hou, Yang Zhang, Jiabao Ji, Yujian Liu, Kaizhi Qian, Jacob Andreas, and Shiyu Chang. Thinkprune: Pruning long chain-of-thought of llms via reinforcement learning. arXiv preprint arXiv:2504.01296, 2025. 4 find
  67. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3, 2022. 9 find
  68. Mengkang Hu, Tianxing Chen, Qiguang Chen, Yao Mu, Wenqi Shao, and Ping Luo. Hiagent: Hierarchical working memory management for solving long-horizon agent tasks with large language model. arXiv preprint arXiv:2408.09559, 2024. 20 find
  69. Mengkang Hu, Yao Mu, Xinmiao Yu, Mingyu Ding, Shiguang Wu, Wenqi Shao, Qiguang Chen, Bin Wang, Yu Qiao, and Ping Luo. Tree-planner: Efficient close-loop task planning with large language models. arXiv preprint arXiv:2310.08582, 2023. 20 — [arXiv:2310.08582](https://arxiv.org/abs/2310.08582) find
  70. Chengsong Huang, Langlin Huang, Jixuan Leng, Jiacheng Liu, and Jiaxin Huang. Efficient test-time scaling via self-calibration. arXiv preprint arXiv:2503.00031, 2025. 4, 13 — [arXiv:2503.00031](https://arxiv.org/abs/2503.00031) find
  71. Jiameng Huang, Baijiong Lin, Guhao Feng, Jierun Chen, Di He, and Lu Hou. Efficient reasoning for large reasoning language models via certainty-guided reflection suppression, 2025. 4 find
  72. Joonwon Jang, Jaehee Kim, Wonbin Kweon, Seonghyeon Lee, and Hwanjo Yu. Verbosity-aware rationale reduction: Effective reduction of redundant rationale via principled criteria, 2025. 4 find
  73. Guochao Jiang, Guofeng Quan, Zepeng Ding, Ziqin Luo, Dixuan Wang, and Zheng Hu. Flashthink: An early exit method for efficient reasoning. arXiv preprint arXiv:2505.13949, 2025. 4 — [arXiv:2505.13949](https://arxiv.org/abs/2505.13949) find
  74. Lingjie Jiang, Xun Wu, Shaohan Huang, Qingxiu Dong, Zewen Chi, Li Dong, Xingxing Zhang, Tengchao Lv, Lei Cui, and Furu Wei. Think only when you need with large hybrid-reasoning models, 2025. 4 find
  75. Yuxuan Jiang, Dawei Li, and Frank Ferraro. Drp: Distilled reasoning pruning with skill-aware step decomposition for efficient large reasoning models. arXiv preprint arXiv:2505.13975, 2025. 4 find
  76. Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, and Mengnan Du. The impact of reasoning step length on large language models. arXiv preprint arXiv:2401.04925, 2024. 4, 18 find
  77. Zhensheng Jin, Xinze Li, Yifan Ji, Chunyi Peng, Zhenghao Liu, Qi Shi, Yukun Yan, Shuo Wang, Furong Peng, and Ge Yu. Recut: Balancing reasoning length and accuracy in llms via stepwise trails and preference optimization, 2025. 4 find
  78. Yu Kang, Xianghui Sun, Liangyu Chen, and Wei Zou. C3ot: Generating shorter chain-of-thought without compromising effectiveness. arXiv preprint arXiv:2412.11664, 2024. 4, 8, 9 find
  79. Henrik Klagges, Robert Dahlke, Fabian Klemm, Benjamin Merkel, Daniel Klingmann, David A Reiss, and Dan Zecha. Assembly of experts: Linear-time construction of the chimera llm variants with emergent and adaptable behaviors. arXiv preprint arXiv:2506.14794, 2025. 4 — [arXiv:2506.14794](https://arxiv.org/abs/2506.14794) find
  80. Levente Kocsis and Csaba Szepesvári. Bandit based monte-carlo planning. In European conference on machine learning, pages 282–293. Springer, 2006. 5 find
  81. Abhinav Kumar, Jaechul Roh, Ali Naseh, Marzena Karpinska, Mohit Iyyer, Amir Houmansadr, and Eugene Bagdasarian. Overthink: Slowdown attacks on reasoning llms. arXiv e-prints, pages arXiv–2502, 2025. 19 find
  82. Martin Kuo, Jianyi Zhang, Aolin Ding, Qinsi Wang, Louis DiValentin, Yujia Bao, Wei Wei, Da-Cheng Juan, Hai Li, and Yiran Chen. H-cot: Hijacking the chain-of-thought safety reasoning mechanism to jailbreak large reasoning models, including openai o1/o3, deepseek-r1, and gemini 2.0 flash thinking. arX… — [arXiv:2502.12893](https://arxiv.org/abs/2502.12893) find
  83. Ayeong Lee, Ethan Che, and Tianyi Peng. How well do llms compress their own chain-of-thought? a token complexity approach. arXiv preprint arXiv:2503.01141, 2025. 4, 14 — [arXiv:2503.01141](https://arxiv.org/abs/2503.01141) find
  84. Byeongchan Lee, Jonghoon Lee, Dongyoung Kim, Jaehyung Kim, and Jinwoo Shin. Collaborative llm inference via planning for efficient reasoning, 2025. 4 find
  85. Celine Lee, Alexander M. Rush, and Keyon Vafa. Critical thinking: Which kinds of complexity govern optimal reasoning length?, 2025. 18 find
  86. Chenghao Li, Chaoning Zhang, Yi Lu, Jiaquan Zhang, Qigan Sun, Xudong Wang, Jiwei Wei, Guoqing Wang, Yang Yang, and Heng Tao Shen. Syzygy of thoughts: Improving llm cot with the minimal free resolution. arXiv preprint arXiv:2504.09566, 2025. 19 find
  87. Gengyang Li, Yifeng Gao, Yuming Li, and Yunfang Wu. Thinkless: A training-free inference-efficient method for reducing reasoning redundancy. arXiv preprint arXiv:2505.15684, 2025. 4 — [arXiv:2505.15684](https://arxiv.org/abs/2505.15684) find
  88. Junyan Li, Wenshuo Zhao, Yang Zhang, and Chuang Gan. Steering llm thinking with budget guidance, 2025. 4 find
  89. Peiji Li, Kai Lv, Yunfan Shao, Yichuan Ma, Linyang Li, Xiaoqing Zheng, Xipeng Qiu, and Qipeng Guo. Fastmcts: A simple sampling strategy for data synthesis. arXiv preprint arXiv:2502.11476, 2025. 4, 11, 12 find
  90. Ruosen Li, Ziming Luo, Quan Zhang, Ruochen Li, Ben Zhou, Ali Payani, and Xinya Du. Aalc: Large language model efficient reasoning via adaptive accuracy-length control, 2025. 4 find
  91. Xuying Li, Zhuo Li, Yuji Kosuga, and Victor Bian. Output length effect on deepseek-r1’s safety in forced thinking. arXiv preprint arXiv:2503.01923, 2025. 19 — [arXiv:2503.01923](https://arxiv.org/abs/2503.01923) find
  92. Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan, Xinglin Wang, Bin Sun, Heda Wang, and Kan Li. Escape sky-high cost: Early-stopping self-consistency for multi-step reasoning. In ICLR, 2024. 4 find
  93. Yuetai Li, Xiang Yue, Zhangchen Xu, Fengqing Jiang, Luyao Niu, Bill Yuchen Lin, Bhaskar Ramasubramanian, and Radha Poovendran. Small models struggle to learn from strong reasoners. arXiv preprint arXiv:2502.12143, 2025. 4, 16, 17 find
  94. Zeju Li, Jianyuan Zhong, Ziyang Zheng, Xiangyu Wen, Zhijian Xu, Yingying Cheng, Fan Zhang, and Qiang Xu. Compressing chain-of-thought in llms via step entropy, 2025. 4 find
  95. Zheng Li, Qingxiu Dong, Jingyuan Ma, Di Zhang, and Zhifang Sui. Selfbudgeter: Adaptive token allocation for efficient llm reasoning. arXiv preprint arXiv:2505.11274, 2025. 4 find
  96. Zhong-Zhi Li, Xiao Liang, Zihao Tang, Lei Ji, Peijie Wang, Haotian Xu, Xing W, Haizhen Huang, Weiwei Deng, Yeyun Gong, Zhijiang Guo, Xiao Liu, Fei Yin, and Cheng-Lin Liu. Tl;dr: Too long, do re-weighting for efficient llm reasoning compression, 2025. 4 find
  97. Zhong-Zhi Li, Duzhen Zhang, Ming-Liang Zhang, Jiaxin Zhang, Zengyan Liu, Yuxuan Yao, Haotian Xu, Junhao Zheng, Pei-Jie Wang, Xiuyi Chen, et al. From system 1 to system 2: A survey of reasoning large language models. arXiv preprint arXiv:2502.17419, 2025. 1 find our card
  98. Guosheng Liang, Longguang Zhong, Ziyi Yang, and Xiaojun Quan. Thinkswitcher: When to think hard, when to think fast, 2025. 4 find
  99. Baohao Liao, Hanze Dong, Yuhui Xu, Doyen Sahoo, Christof Monz, Junnan Li, and Caiming Xiong. Fractured chain-of-thought reasoning. arXiv preprint arXiv:2505.12992, 2025. 4 — [arXiv:2505.12992](https://arxiv.org/abs/2505.12992) find
  100. Baohao Liao, Yuhui Xu, Hanze Dong, Junnan Li, Christof Monz, Silvio Savarese, Doyen Sahoo, and Caiming Xiong. Reward-guided speculative decoding for efficient llm reasoning. arXiv preprint arXiv:2501.19324, 2025. 4, 11, 12 — [arXiv:2501.19324](https://arxiv.org/abs/2501.19324) find
  101. Huanxuan Liao, Shizhu He, Yupu Hao, Xiang Li, Yuanzhe Zhang, Jun Zhao, and Kang Liu. Skintern: Internalizing symbolic knowledge for distilling better cot capabilities into small language models. In Proceedings of the 31st International Conference on Computational Linguistics, pages 3203–3221, 2025.… find
  102. Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, and Song Han. Awq: Activation-aware weight quantization for on-device llm compression and acceleration. Proceedings of Machine Learning and Systems, 6:87–100, 2024. 2 find
  103. Junhong Lin, Xinyue Zeng, Jie Zhu, Song Wang, Julian Shun, Jun Wu, and Dawei Zhou. Plan and budget: Effective and efficient test-time scaling on large language model reasoning. arXiv preprint arXiv:2505.16122, 2025. 4 — [arXiv:2505.16122](https://arxiv.org/abs/2505.16122) find
  104. Kevin Lin, Charlie Snell, Yu Wang, Charles Packer, Sarah Wooders, Ion Stoica, and Joseph E Gonzalez. Sleep-time compute: Beyond inference scaling at test-time. arXiv preprint arXiv:2504.13171, 2025. 4 — [arXiv:2504.13171](https://arxiv.org/abs/2504.13171) find
  105. Weizhe Lin, Xing Li, Zhiyuan Yang, Xiaojin Fu, Hui-Ling Zhen, Yaoyuan Wang, Xianzhi Yu, Wulong Liu, Xiaosong Li, and Mingxuan Yuan. Trimr: Verifier-based training-free thinking compression for efficient test-time scaling. arXiv preprint arXiv:2505.17155, 2025. 4 — [arXiv:2505.17155](https://arxiv.org/abs/2505.17155) find
  106. Zhengkai Lin, Zhihang Fu, Ze Chen, Chao Chen, Liang Xie, Wenxiao Wang, Deng Cai, Zheng Wang, and Jieping Ye. Controlling thinking speed in reasoning models, 2025. 4 find
  107. Zehui Ling, Deshu Chen, Hongwei Zhang, Yifeng Jiao, Xin Guo, and Yuan Cheng. Fast on the easy, deep on the hard: Efficient reasoning via powered length penalty, 2025. 4 find
  108. Bang Liu, Xinfeng Li, Jiayi Zhang, Jinlin Wang, Tanjin He, Sirui Hong, Hongzhang Liu, Shaokun Zhang, Kaitao Song, Kunlun Zhu, et al. Advances and challenges in foundation agents: From brain-inspired intelligence to evolutionary, collaborative, and safe systems. arXiv preprint arXiv:2504.01990, 2025.… — [arXiv:2504.01990](https://arxiv.org/abs/2504.01990) find
  109. Fan Liu, Wenshuo Chao, Naiqiang Tan, and Hao Liu. Bag of tricks for inference-time computation of llm reasoning. arXiv preprint arXiv:2502.07191, 2025. 4, 17 find
  110. Hanbing Liu, Lang Cao, Yuanyi Ren, Mengyu Zhou, Haoyu Dong, Xiaojun Ma, Shi Han, and Dongmei Zhang. Bingo: Boosting efficient reasoning of llms via dynamic and significance-based reinforcement learning. arXiv preprint arXiv:2506.08125, 2025. 4 — [arXiv:2506.08125](https://arxiv.org/abs/2506.08125) find
  111. Kaiyuan Liu, Chen Shen, Zhanwei Zhang, Junjie Liu, Xiaosong Yuan, and Jieping ye. Efficient reasoning through suppression of self-affirmation reflections in large reasoning models, 2025. 4 find
  112. Ruikang Liu, Yuxuan Sun, Manyi Zhang, Haoli Bai, Xianzhi Yu, Tiezheng Yu, Chun Yuan, and Lu Hou. Quantization hurts reasoning? an empirical study on quantized reasoning models, 2025. 4, 18 find
  113. Runze Liu, Junqi Gao, Jian Zhao, Kaiyan Zhang, Xiu Li, Biqing Qi, Wanli Ouyang, and Bowen Zhou. Can 1b llm surpass 405b llm? rethinking compute-optimal test-time scaling, 2025. 4, 17 find
  114. Ryan Liu, Jiayi Geng, Addison J Wu, Ilia Sucholutsky, Tania Lombrozo, and Thomas L Griffiths. Mind your step (by step): Chain-of-thought can reduce performance on tasks where thinking makes humans worse. arXiv preprint arXiv:2410.21333, 2024. 18 — [arXiv:2410.21333](https://arxiv.org/abs/2410.21333) find
  115. Tengxiao Liu, Qipeng Guo, Xiangkun Hu, Cheng Jiayang, Yue Zhang, Xipeng Qiu, and Zheng Zhang. Can language models learn to skip steps? arXiv preprint arXiv:2411.01855, 2024. 4, 8, 9 — [arXiv:2411.01855](https://arxiv.org/abs/2411.01855) find
  116. Wei Liu, Ruochen Zhou, Yiyun Deng, Yuzhen Huang, Junteng Liu, Yuntian Deng, Yizhe Zhang, and Junxian He. Learn to reason efficiently with adaptive length-based reward shaping. arXiv preprint arXiv:2505.15612, 2025. 4 — [arXiv:2505.15612](https://arxiv.org/abs/2505.15612) find
  117. Xin Liu and Lu Wang. Answer convergence as a signal for early stopping in reasoning, 2025. 4 find
  118. Yule Liu, Jingyi Zheng, Zhen Sun, Zifan Peng, Wenhan Dong, Zeyang Sha, Shiwen Cui, Weiqiang Wang, and Xinlei He. Thought manipulation: External thought can be efficient for large reasoning models, 2025. 4 find
  119. Yuliang Liu, Junjie Lu, Zhaoling Chen, Chaofeng Qu, Jason Klein Liu, Chonghan Liu, Zefan Cai, Yunhui Xia, Li Zhao, Jiang Bian, et al. Adaptivestep: Automatically dividing reasoning step through model confidence. arXiv preprint arXiv:2502.13943, 2025. 4 — [arXiv:2502.13943](https://arxiv.org/abs/2502.13943) find
  120. Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, and Xia Hu. Kivi: A tuning-free asymmetric 2bit quantization for kv cache. arXiv preprint arXiv:2402.02750, 2024. 2 find our card
  121. Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. 7 find
  122. Chenwei Lou, Zewei Sun, Xinnian Liang, Meng Qu, Wei Shen, Wenqi Wang, Yuntao Li, Qingping Yang, and Shuangzhi Wu. Adacot: Pareto-optimal adaptive chain-of-thought triggering via reinforcement learning, 2025. 4 find
  123. Jinghui Lu, Haiyang Yu, Siliang Xu, Shiwei Ran, Guozhi Tang, Siqi Wang, Bin Shan, Teng Fu, Hao Feng, Jingqun Tang, Han Wang, and Can Huang. Prolonged reasoning is not all you need: Certainty-based adaptive routing for efficient llm/mllm reasoning, 2025. 4 find
  124. Ximing Lu, Seungju Han, David Acuna, Hyunwoo Kim, Jaehun Jung, Shrimai Prabhumoye, Niklas Muennighoff, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, and Yejin Choi. Retro-search: Exploring untaken paths for deeper and efficient reasoning, 2025. 4 find
  125. Feng Luo, Yu-Neng Chuang, Guanchu Wang, Hoang Anh Duy Le, Shaochen Zhong, Hongyi Liu, Jiayi Yuan, Yang Sui, Vladimir Braverman, Vipin Chaudhary, et al. Autol2s: Auto long-short reasoning for efficient large language models. arXiv preprint arXiv:2505.22662, 2025. 4 find
  126. Haotian Luo, Haiying He, Yibo Wang, Jinluan Yang, Rui Liu, Naiqiang Tan, Xiaochun Cao, Dacheng Tao, and Li Shen. Ada-r1: Hybrid-cot via bi-level adaptive reasoning optimization. arXiv preprint arXiv:2504.21659, 2025. 4 — [arXiv:2504.21659](https://arxiv.org/abs/2504.21659) find
  127. Haotian Luo, Li Shen, Haiying He, Yibo Wang, Shiwei Liu, Wei Li, Naiqiang Tan, Xiaochun Cao, and Dacheng Tao. O1-pruner: Length-harmonizing fine-tuning for o1-like reasoning pruning. arXiv preprint arXiv:2501.12570, 2025. 2, 4, 6, 7 find
  128. Ruotian Ma, Peisong Wang, Cheng Liu, Xingyan Liu, Jiaqi Chen, Bang Zhang, Xin Zhou, Nan Du, and Jia Li. S 2 r: Teaching llms to self-verify and self-correct via reinforcement learning, 2025. 4, 16 find
  129. Wenjie Ma, Jingxuan He, Charlie Snell, Tyler Griggs, Sewon Min, and Matei Zaharia. Reasoning models can be effective without thinking. arXiv preprint arXiv:2504.09858, 2025. 4 find
  130. Xinyin Ma, Guangnian Wan, Runpeng Yu, Gongfan Fang, and Xinchao Wang. Cot-valve: Length-compressible chain-of-thought tuning. arXiv preprint arXiv:2502.09601, 2025. 2, 4, 8, 9 find
  131. Yu Meng, Mengzhou Xia, and Danqi Chen. Simpo: Simple preference optimization with a reference-free reward. Advances in Neural Information Processing Systems, 37:124198– 124235, 2024. 6, 7 find
  132. Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. s1: Simple test-time scaling, 2025. 4, 16 find our card
  133. Tergel Munkhbat, Namgyu Ho, Seohyun Kim, Yongjin Yang, Yujin Kim, and Se-Young Yun. Self-training elicits concise reasoning in large language models. arXiv preprint arXiv:2502.20122, 2025. 4, 8, 9 find
  134. Yansong Ning, Wei Li, Jun Fang, Naiqiang Tan, and Hao Liu. Not all thoughts are generated equal: Efficient llm reasoning via multi-turn reinforcement learning. arXiv preprint arXiv:2505.11827, 2025. 4 find
  135. Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E Gonzalez, M Waleed Kadous, and Ion Stoica. Routellm: Learning to route llms with preference data. arXiv preprint arXiv:2406.18665, 2024. 15 find
  136. Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E. Gonzalez, M Waleed Kadous, and Ion Stoica. Routellm: Learning to route llms with preference data, 2025. 4 find
  137. OpenAI. Learning to reason with llms. urlhttps://openai.com/index/learning-to-reason-with-llms/. Accessed: 15 March 2025. 1, 4, 6 find
  138. Rui Pan, Yinwei Dai, Zhihao Zhang, Gabriele Oliaro, Zhihao Jia, and Ravi Netravali. Specreason: Fast and accurate inference-time compute via speculative reasoning. arXiv preprint arXiv:2504.07891, 2025. 4 — [arXiv:2504.07891](https://arxiv.org/abs/2504.07891) find
  139. Zhenyu Pan, Haozheng Luo, Manling Li, and Han Liu. Chain-of-action: Faithful and multimodal question answering through large language models. arXiv preprint arXiv:2403.17359, 2024. 20 — [arXiv:2403.17359](https://arxiv.org/abs/2403.17359) find
  140. Shubham Parashar, Blake Olson, Sambhav Khurana, Eric Li, Hongyi Ling, James Caverlee, and Shuiwang Ji. Inference-time computations for llm reasoning and planning: A benchmark and insights, 2025. 4, 17 find
  141. Jacob Pfau, William Merrill, and Samuel R Bowman. Let’s think dot by dot: Hidden computation in transformer language models. arXiv preprint arXiv:2404.15758, 2024. 9 find
  142. Soham Poddar, Paramita Koley, Janardan Misra, Sanjay Podder, Navveen Balani, Niloy Ganguly, and Saptarshi Ghosh. Brevity is the soul of sustainability: Characterizing llm response lengths, 2025. 4 find
  143. Xiao Pu, Michael Saxon, Wenyue Hua, and William Yang Wang. Thoughtterminator: Benchmarking, calibrating, and mitigating overthinking in reasoning models, 2025. 4 find
  144. Penghui Qi, Zichen Liu, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin. Optimizing anytime reasoning via budget relative policy optimization. arXiv preprint arXiv:2505.13438, 2025. 4 — [arXiv:2505.13438](https://arxiv.org/abs/2505.13438) find
  145. Ziqing Qiao, Yongheng Deng, Jiali Zeng, Dong Wang, Lai Wei, Fandong Meng, Jie Zhou, Ju Ren, and Yaoxue Zhang. Concise: Confidence-guided compression in step-by-step efficient reasoning. arXiv preprint arXiv:2505.04881, 2025. 4 find
  146. Yuxiao Qu, Matthew YR Yang, Amrith Setlur, Lewis Tunstall, Edward Emanuel Beeching, Ruslan Salakhutdinov, and Aviral Kumar. Optimizing test-time compute via meta reinforcement fine-tuning. arXiv preprint arXiv:2503.07572, 2025. 4 — [arXiv:2503.07572](https://arxiv.org/abs/2503.07572) find
  147. Matthew Renze and Erhan Guven. 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. IEEE, 2024. 4, 14, 15 find
  148. Kusha Sareen, Morgane M Moss, Alessandro Sordoni, Rishabh Agarwal, and Arian Hosseini. Putting the value back in rl: Better test-time scaling by unifying llm reasoners with verifiers, 2025. 4 find
  149. Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li, Sanjiv Kumar, and Sashank J Reddi. Reasoning with latent thoughts: On the power of looped transformers. arXiv preprint arXiv:2502.17416, 2025. 4, 10 find
  150. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. 6, 7 find
  151. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. 6 find our card
  152. Jianshu She, Zhuohao Li, Zhemin Huang, Qi Li, Peiran Xu, Haonan Li, and Qirong Ho. Hawkeye:efficient reasoning with model collaboration, 2025. 4 find
  153. Xuan Shen, Yizhou Wang, Xiangxi Shi, Yanzhi Wang, Pu Zhao, and Jiuxiang Gu. Efficient reasoning with hidden thinking. arXiv preprint arXiv:2501.19201, 2025. 4, 10 — [arXiv:2501.19201](https://arxiv.org/abs/2501.19201) find
  154. Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi, Wenjing Zhang, Jiangze Yan, Ning Wang, Kai Wang, and Shiguo Lian. Dast: Difficulty-adaptive slow-thinking for large reasoning models. arXiv preprint arXiv:2503.04472, 2025. 4, 6, 7 find
  155. Zhenyi Shen, Hanqi Yan, Linhai Zhang, Zhanghao Hu, Yali Du, and Yulan He. Codi: Compressing chain-of-thought into continuous space via self-distillation. arXiv preprint arXiv:2502.21074, 2025. 4, 10 find
  156. Luohe Shi, Hongyi Zhang, Yao Yao, Zuchao Li, and Hai Zhao. Keep the cost down: A review on methods to optimize llm’s kv-cache consumption. arXiv preprint arXiv:2407.18003, 2024. 2 find
  157. Vaishnavi Shrivastava, Ahmed Awadallah, Vidhisha Balachandran, Shivam Garg, Harkirat Behl, and Dimitris Papailiopoulos. Sample more to think less: Group filtered policy optimization for concise reasoning. arXiv preprint arXiv:2508.09726, 2025. 4 — [arXiv:2508.09726](https://arxiv.org/abs/2508.09726) find
  158. Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling llm test-time compute optimally can be more effective than scaling model parameters. arXiv preprint arXiv:2408.03314, 2024. 11 find our card
  159. Jiwon Song, Dongwon Jo, Yulhwa Kim, and Jae-Joon Kim. Reasoning path compression: Compressing generation trajectories for efficient llm reasoning. arXiv preprint arXiv:2505.13866, 2025. 4 — [arXiv:2505.13866](https://arxiv.org/abs/2505.13866) find
  160. Mingyang Song and Mao Zheng. Walk before you run! concise llm reasoning via reinforcement learning. arXiv preprint arXiv:2505.21178, 2025. 4 — [arXiv:2505.21178](https://arxiv.org/abs/2505.21178) find
  161. Woomin Song, Saket Dingliwal, Sai Muralidhar Jayanthi, Bhavana Ganesh, Jinwoo Shin, Aram Galstyan, and Sravan Babu Bodapati. Accelerated test-time scaling with model-free speculative sampling. arXiv preprint arXiv:2506.04708, 2025. 4 find
  162. Gaurav Srivastava, Shuxiang Cao, and Xuan Wang. Towards reasoning ability of small language models. arXiv preprint arXiv:2502.11569, 2025. 4, 17 find
  163. DiJia Su, Hanlin Zhu, Yingchen Xu, Jiantao Jiao, Yuandong Tian, and Qinqing Zheng. Token assorted: Mixing latent and text tokens for improved language model reasoning. arXiv preprint arXiv:2502.03275, 2025. 4, 10 — [arXiv:2502.03275](https://arxiv.org/abs/2502.03275) find
  164. Yuan Sui, Yufei He, Tri Cao, Simeng Han, and Bryan Hooi. Meta-reasoner: Dynamic guidance for optimized inference-time reasoning in large language models. arXiv preprint arXiv:2502.19918, 2025. 19 find
  165. Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao Qiu, Ming Yin, Mengdi Wang, Peter Bartlett, and Andrea Zanette. Fast best-of-n decoding via speculative rejection. arXiv preprint arXiv:2410.20290, 2024. 4, 11, 12, 13 find
  166. Lin Sun, Guangxiang Zhao, Xiaoqi Jian, Yuhan Wu, Weihong Lin, Yongfu Zhu, Linglin Zhang, Jinzhu Wu, Junfeng Ran, Sai-er Hu, et al. Tinyr1-32b-preview: Boosting accuracy with branch-merge distillation. arXiv preprint arXiv:2503.04872, 2025. 4 find
  167. Wenhui Tan, Jiaze Li, Jianzhong Ju, Zhenbo Luo, Jian Luan, and Ruihua Song. Think silently, think fast: Dynamic latent compression of llm reasoning chains. arXiv preprint arXiv:2505.16552, 2025. 4 find
  168. Jiakai Tang, Sunhao Dai, Teng Shi, Jun Xu, Xu Chen, Wen Chen, Wu Jian, and Yuning Jiang. Think before recommend: Unleashing the latent reasoning power for sequential recommendation. arXiv preprint arXiv:2503.22675, 2025. 19 — [arXiv:2503.22675](https://arxiv.org/abs/2503.22675) find
  169. Siao Tang, Xinyin Ma, Gongfan Fang, and Xinchao Wang. Concisehint: Boosting efficient reasoning via continuous concise hints during generation, 2025. 4 find
  170. Amir Taubenfeld, Tom Sheffer, Eran Ofek, Amir Feder, Ariel Goldstein, Zorik Gekhman, and Gal Yona. Confidence improves self-consistency in llms. arXiv preprint arXiv:2502.06233, 2025. 4, 13 find
  171. Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, et al. Kimi k1. 5: Scaling reinforcement learning with llms. arXiv preprint arXiv:2501.12599, 2025. 4, 5, 7, 9 find our card
  172. Qwen Team. Qwq-32b-preview. urlhttps://qwenlm.github.io/blog/qwq-32b-preview/. Accessed: 15 March 2025. 5, 6 find
  173. Manan Tomar, Lior Shani, Yonathan Efroni, and Mohammad Ghavamzadeh. Mirror descent policy optimization. arXiv preprint arXiv:2005.09814, 2020. 7 — [arXiv:2005.09814](https://arxiv.org/abs/2005.09814) find
  174. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 1 find
  175. Songjun Tu, Jiahao Lin, Qichao Zhang, Xiangyu Tian, Linjing Li, Xiangyuan Lan, and Dongbin Zhao. Learning when to think: Shaping adaptive reasoning in r1-style models via multi-stage rl, 2025. 4 find
  176. Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. Solving math word problems with process-and outcome-based feedback. arXiv preprint arXiv:2211.14275, 2022. 11 find our card
  177. Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. 10 find
  178. Guangya Wan, Yuqi Wu, Jie Chen, and Sheng Li. Reasoning aware self-consistency: Leveraging reasoning paths for efficient llm sampling. arXiv preprint arXiv:2408.17017, 2024. 4 find
  179. Xu Wan, Wei Wang, Wenyue Xu, Wotao Yin, Jie Song, and Mingyang Sun. Adapthink: Adaptive thinking preferences for reasoning language model, 2025. 4 find
  180. Chenlong Wang, Yuanning Feng, Dongping Chen, Zhaoyang Chu, Ranjay Krishna, and Tianyi Zhou. Wait, we don’t need to "wait"! removing thinking tokens improves reasoning efficiency, 2025. 4 find
  181. Jikai Wang, Juntao Li, Jianye Hou, Bowen Yan, Lijun Wu, and Min Zhang. Efficient reasoning for llms through speculative chain-of-thought, 2025. 4 find
  182. Junlin Wang, Shang Zhu, Jon Saad-Falcon, Ben Athiwaratkun, Qingyang Wu, Jue Wang, Shuaiwen Leon Song, Ce Zhang, Bhuwan Dhingra, and James Zou. Think deep, think fast: Investigating efficiency of verifier-free inference-time-scaling methods, 2025. 4 find
  183. Kaiwen Wang, Jin Peng Zhou, Jonathan Chang, Zhaolin Gao, Nathan Kallus, Kianté Brantley, and Wen Sun. Value-guided search for efficient chain-of-thought reasoning, 2025. 4 find
  184. Xinglin Wang, Shaoxiong Feng, Yiwei Li, Peiwen Yuan, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, and Kan Li. Make every penny count: Difficulty-adaptive self-consistency for cost-efficient reasoning. arXiv preprint arXiv:2408.13457, 2024. 4 find
  185. Xinglin Wang, Yiwei Li, Shaoxiong Feng, Peiwen Yuan, Yueqi Zhang, Jiayi Shi, Chuyi Tan, Boyuan Pan, Yao Hu, and Kan Li. Every rollout counts: Optimal resource allocation for efficient test-time scaling, 2025. 4 find
  186. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, 2023. 3 find our card
  187. Yibo Wang, Li Shen, Huanjin Yao, Tiansheng Huang, Rui Liu, Naiqiang Tan, Jiaxing Huang, Kai Zhang, and Dacheng Tao. R1-compress: Long chain-of-thought compression via chunk compression and search. arXiv preprint arXiv:2505.16838, 2025. 4 — [arXiv:2505.16838](https://arxiv.org/abs/2505.16838) find
  188. Yiming Wang, Pei Zhang, Siyuan Huang, Baosong Yang, Zhuosheng Zhang, Fei Huang, and Rui Wang. Sampling-efficient test-time scaling: Self-estimating the best-of-n sampling in early decoding. arXiv preprint arXiv:2503.01422, 2025. 4, 11, 12, 13 find
  189. Zhihai Wang, Jie Wang, Jilai Pan, Xilin Xia, Huiling Zhen, Mingxuan Yuan, Jianye Hao, and Feng Wu. Accelerating large language model reasoning via speculative search, 2025. 4 find
  190. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824–24837, 2022. 2, 3 find our card
  191. Liang Wen, Yunke Cai, Fenrui Xiao, Xin He, Qi An, Zhenyu Duan, Yimin Du, Junchen Liu, Lifu Tang, Xiaowei Lv, et al. Light-r1: Curriculum sft, dpo and rl for long cot from scratch and beyond. arXiv preprint arXiv:2503.10460, 2025. 4 find
  192. Han Wu, Yuxuan Yao, Shuqi Liu, Zehua Liu, Xiaojin Fu, Xiongwei Han, Xing Li, Hui-Ling Zhen, Tao Zhong, and Mingxuan Yuan. Unlocking efficient long-to-short llm reasoning with model merging, 2025. 9 find
  193. Yuyang Wu, Yifei Wang, Tianqi Du, Stefanie Jegelka, and Yisen Wang. When more is less: Understanding chain-of-thought length in llms. arXiv preprint arXiv:2502.07266, 2025. 4, 12, 13 find
  194. Heming Xia, Yongqi Li, Chak Tou Leong, Wenjie Wang, and Wenjie Li. Tokenskip: Controllable chain-of-thought compression in llms. arXiv preprint arXiv:2502.12067, 2025. 4, 8, 9 find
  195. Kun Xiang, Zhili Liu, Zihao Jiang, Yunshuang Nie, Kaixin Cai, Yiyang Yin, Runhui Huang, Haoxiang Fan, Hanhui Li, Weiran Huang, et al. Can atomic step decomposition enhance the self-structured reasoning of multimodal large models? arXiv preprint arXiv:2503.06252, 2025. 4 — [arXiv:2503.06252](https://arxiv.org/abs/2503.06252) find
  196. Violet Xiang, Chase Blagden, Rafael Rafailov, Nathan Lile, Sang Truong, Chelsea Finn, and Nick Haber. Just enough thinking: Efficient reasoning with adaptive length penalties reinforcement learning, 2025. 4 find
  197. Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant: Accurate and efficient post-training quantization for large language models. In International Conference on Machine Learning, pages 38087–38099. PMLR, 2023. 2 find
  198. Yang Xiao, Jiashuo Wang, Ruifeng Yuan, Chunpu Xu, Kaishuai Xu, Wenjie Li, and Pengfei Liu. Limopro: Reasoning refinement for efficient and effective test-time scaling. arXiv preprint arXiv:2505.19187, 2025. 4 — [arXiv:2505.19187](https://arxiv.org/abs/2505.19187) find
  199. Shuo Xing, Hongyuan Hua, Xiangbo Gao, Shenzhe Zhu, Renjie Li, Kexin Tian, Xiaopeng Li, Heng Huang, Tianbao Yang, Zhangyang Wang, et al. Autotrust: Benchmarking trustworthiness in large vision language models for autonomous driving. arXiv preprint arXiv:2412.15206, 2024. 18 — [arXiv:2412.15206](https://arxiv.org/abs/2412.15206) find
  200. Shuo Xing, Chengyuan Qian, Yuping Wang, Hongyuan Hua, Kexin Tian, Yang Zhou, and Zhengzhong Tu. Openemma: Open-source multimodal model for end-to-end autonomous driving. In Proceedings of the Winter Conference on Applications of Computer Vision, pages 1001–1009, 2025. 18 find
  201. Shuo Xing, Zezhou Sun, Shuangyu Xie, Kaiyuan Chen, Yanjia Huang, Yuping Wang, Jiachen Li, Dezhen Song, and Zhengzhong Tu. Can large vision language models read maps like a human? arXiv preprint arXiv:2503.14607, 2025. 18 — [arXiv:2503.14607](https://arxiv.org/abs/2503.14607) find
  202. Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, et al. Towards large reasoning models: A survey of reinforced reasoning with large language models. arXiv preprint arXiv:2501.09686, 2025. 1 find
  203. Jingxian Xu, Mengyu Zhou, Weichang Liu, Hanbing Liu, Shi Han, and Dongmei Zhang. Twt: Thinking without tokens by habitual reasoning distillation with multi-teachers’ guidance, 2025. 4 find
  204. Silei Xu, Wenhao Xie, Lingxiao Zhao, and Pengcheng He. Chain of draft: Thinking faster by writing less. arXiv preprint arXiv:2502.18600, 2025. 4, 14 find
  205. Xiaoang Xu, Shuo Wang, Xu Han, Zhenghao Liu, Huijia Wu, Peipei Li, Zhiyuan Liu, Maosong Sun, and Zhaofeng He. A*-thought: Efficient reasoning via bidirectional compression for low-resource settings, 2025. 4 find
  206. Yige Xu, Xu Guo, Zhiwei Zeng, and Chunyan Miao. Softcot: Soft chain-of-thought for efficient reasoning with llms. arXiv preprint arXiv:2502.12134, 2025. 4, 10 find
  207. Yuhui Xu, Hanze Dong, Lei Wang, Doyen Sahoo, Junnan Li, and Caiming Xiong. Scalable chain of thoughts via elastic reasoning. arXiv preprint arXiv:2505.05315, 2025. 4 — [arXiv:2505.05315](https://arxiv.org/abs/2505.05315) find
  208. Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Luu Anh Tuan, Haiteng Zhao, Qika Lin, and Jun Liu. Mur: Momentum uncertainty guided reasoning for large language models, 2025. 4 find
  209. Yuchen Yan, Yongliang Shen, Yang Liu, Jin Jiang, Mengdi Zhang, Jian Shao, and Yueting Zhuang. Inftythink: Breaking the length limits of long-context reasoning in large language models. arXiv preprint arXiv:2503.06692, 2025. 4, 12, 13 find
  210. Chenxu Yang, Qingyi Si, Mz Dai, Dingyu Yao, Mingyu Zheng, Minghui Chen, Zheng Lin, and Weiping Wang. Test-time prompt intervention, 2025. 4 find
  211. Enneng Yang, Li Shen, Guibing Guo, Xingwei Wang, Xiaochun Cao, Jie Zhang, and Dacheng Tao. Model merging in llms, mllms, and beyond: Methods, theories, applications and opportunities, 2024. 9 find
  212. Junjie Yang, Ke Lin, and Xing Yu. Think when you need: Self-adaptive chain-of-thought learning, 2025. 4 find
  213. Wang Yang, Xiang Yue, Vipin Chaudhary, and Xiaotian Han. Speculative thinking: Enhancing small-model reasoning with large model guidance at inference time. arXiv preprint arXiv:2504.12329, 2025. 4 — [arXiv:2504.12329](https://arxiv.org/abs/2504.12329) find
  214. Wenkai Yang, Shuming Ma, Yankai Lin, and Furu Wei. Towards thinking-optimal scaling of test-time compute for llm reasoning, 2025. 4 find
  215. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. Advances in neural information processing systems, 36:11809–11822, 2023. 3 find
  216. Yixin Ye, Zhen Huang, Yang Xiao, Ethan Chern, Shijie Xia, and Pengfei Liu. Limo: Less is more for reasoning, 2025. 4, 16 find our card
  217. Edward Yeo, Yuxuan Tong, Morry Niu, Graham Neubig, and Xiang Yue. Demystifying long chain-of-thought reasoning in llms. arXiv preprint arXiv:2502.03373, 2025. 2, 4, 6, 7, 18 find our card
  218. Bin Yu, Hang Yuan, Haotian Li, Xueyin Xu, Yuliang Wei, Bailing Wang, Weizhen Qi, and Kai Chen. Long-short chain-of-thought mixture supervised fine-tuning eliciting efficient reasoning in large language models. arXiv preprint arXiv:2505.03469, 2025. 4 find
  219. Ping Yu, Jing Xu, Jason Weston, and Ilia Kulikov. Distilling system 2 into system 1. arXiv preprint arXiv:2407.06023, 2024. 4, 8, 9 find
  220. Xiangning Yu, Zhuohan Wang, Linyi Yang, Haoxuan Li, Anjie Liu, Xiao Xue, Jun Wang, and Mengyue Yang. Causal sufficiency and necessity improves chain-of-thought reasoning, 2025. 4 find
  221. Ye Yu, Yaoning Yu, and Haohan Wang. Premise: Scalable and strategic prompt optimization for efficient mathematical reasoning in large models, 2025. 4 find
  222. Zeping Yu, Yonatan Belinkov, and Sophia Ananiadou. Back attention: Understanding and enhancing multi-hop reasoning in large language models. arXiv preprint arXiv:2502.10835, 2025. 4 find
  223. Zhaojian Yu, Yinghao Wu, Yilun Zhao, Arman Cohan, and Xiao-Ping Zhang. Z1: Efficient test-time scaling with code, 2025. 4 find
  224. Zishun Yu, Tengyu Xu, Di Jin, Karthik Abinav Sankararaman, Yun He, Wenxuan Zhou, Zhouhao Zeng, Eryk Helenowski, Chen Zhu, Sinong Wang, et al. Think smarter not harder: Adaptive reasoning with inference aware optimization. arXiv preprint arXiv:2501.17974, 2025. 4 — [arXiv:2501.17974](https://arxiv.org/abs/2501.17974) find
  225. Danlong Yuan, Tian Xie, Shaohan Huang, Zhuocheng Gong, Huishuai Zhang, Chong Luo, Furu Wei, and Dongyan Zhao. Efficient rl training for reasoning models via length-aware optimization. arXiv preprint arXiv:2505.12284, 2025. 4 — [arXiv:2505.12284](https://arxiv.org/abs/2505.12284) find
  226. Hang Yuan, Bin Yu, Haotian Li, Shijun Yang, Christina Dan Wang, Zhou Yu, Xueyin Xu, Weizhen Qi, and Kai Chen. Not all tokens are what you need in thinking. arXiv preprint arXiv:2505.17827, 2025. 4 — [arXiv:2505.17827](https://arxiv.org/abs/2505.17827) find
  227. Jiayi Yuan, Hongyi Liu, Shaochen Zhong, Yu-Neng Chuang, Songchen Li, Guanchu Wang, Duy Le, Hongye Jin, Vipin Chaudhary, Zhaozhuo Xu, Zirui Liu, and Xia Hu. KV cache compression, but what must we give in return? a comprehensive benchmark of long context capable approaches. In Findings of the Associat… find
  228. Chuhuai Yue, Chengqi Dong, Yinan Gao, Hang He, Jiajun Chai, Guojun Yin, and Wei Lin. Promoting efficient reasoning with verifiable stepwise reward, 2025. 4 find
  229. Wenhao Zeng, Yaoning Wang, Chao Hu, Yuling Shi, Chengcheng Wan, Hongyu Zhang, and Xiaodong Gu. Pruning the unsurprising: Efficient code reasoning via first-token surprisal, 2025. 4 find
  230. Jiajie Zhang, Nianyi Lin, Lei Hou, Ling Feng, and Juanzi Li. Adaptthink: Reasoning models can learn when to think, 2025. 4 find
  231. Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, and Ningyu Zhang. Lightthinker: Thinking step-by-step compression. arXiv preprint arXiv:2502.15589, 2025. 4, 12, 13 find
  232. Junyu Zhang, Runpei Dong, Han Wang, Xuying Ning, Haoran Geng, Peihao Li, Xialin He, Yutong Bai, Jitendra Malik, Saurabh Gupta, et al. Alphaone: Reasoning models thinking slow and fast at test time. arXiv preprint arXiv:2505.24863, 2025. 4 find
  233. Nan Zhang, Yusen Zhang, Prasenjit Mitra, and Rui Zhang. When reasoning meets compression: Benchmarking compressed large reasoning models on complex reasoning tasks. arXiv preprint arXiv:2504.02010, 2025. 4, 18 — [arXiv:2504.02010](https://arxiv.org/abs/2504.02010) find
  234. Ruiqi Zhang, Changyi Xiao, and Yixin Cao. Long or short cot? investigating instance-level switch of large reasoning models, 2025. 4 find
  235. Shengjia Zhang, Junjie Wu, Jiawei Chen, Changwang Zhang, Xingyu Lou, Wangchunshu Zhou, Sheng Zhou, Can Wang, and Jun Wang. Othink-r1: Intrinsic fast/slow thinking mode switching for over-reasoning mitigation, 2025. 4 find
  236. Wencheng Zhang, Shiqin Qiao, Lingjie Luo, Yinfeng Li, Chuanyang Zheng, Qian Xu, Meng Li, Yong Gui, Yijun He, Jianing Qiu, Jindong Hong, and Jiankai Sun. Synapseroute: An auto-route switching framework on dual-state large language model, 2025. 4 find
  237. Wenyuan Zhang, Shuaiyi Nie, Xinghua Zhang, Zefeng Zhang, and Tingwen Liu. S1-bench: A simple benchmark for evaluating system 1 thinking capability of large reasoning models. arXiv preprint arXiv:2504.10368, 2025. 4, 18 — [arXiv:2504.10368](https://arxiv.org/abs/2504.10368) find
  238. Xiaoyun Zhang, Jingqing Ruan, Xing Ma, Yawen Zhu, Haodong Zhao, Hao Li, Jiansong Chen, Ke Zeng, and Xunliang Cai. When to continue thinking: Adaptive thinking mode switching for efficient reasoning, 2025. 4 find
  239. Xuechen Zhang, Zijian Huang, Chenshun Ni, Ziyang Xiong, Jiasi Chen, and Samet Oymak. Making small language models efficient reasoners: Intervention, supervision, reinforcement. arXiv preprint arXiv:2505.07961, 2025. 4 find
  240. Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim, Moontae Lee, Honglak Lee, and Lu Wang. Small language models need strong verifiers to self-correct reasoning. arXiv preprint arXiv:2404.17140, 2024. 17 find
  241. Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, et al. H2o: Heavy-hitter oracle for efficient generative inference of large language models. Advances in Neural Information Processing Systems, 36:34661–34710, 202… find
  242. Kai Zhao, Yanjun Zhao, Jiaming Song, Shien He, Lusheng Zhang, Qiang Zhang, and Tianjiao Li. Saber: Switchable and balanced training for efficient llm reasoning, 2025. 4 find
  243. Shangziqi Zhao, Jiahao Yuan, Guisong Yang, and Usman Naseem. Can pruning improve reasoning? revisiting long-cot compression with capability in mind for better reasoning. arXiv preprint arXiv:2505.14582, 2025. 4 — [arXiv:2505.14582](https://arxiv.org/abs/2505.14582) find
  244. Weixiang Zhao, Jiahe Guo, Yang Deng, Xingyu Sui, Yulin Hu, Yanyan Zhao, Wanxiang Che, Bing Qin, Tat-Seng Chua, and Ting Liu. Exploring and exploiting the inherent efficiency within large reasoning models for self-guided efficiency enhancement, 2025. 4 find
  245. Yichun Zhao, Shuheng Zhou, and Huijia Zhu. Probe then retrieve and reason: Distilling probing and reasoning capabilities into smaller language models. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 1… find
  246. Zhi Zhou, Tan Yuhao, Zenan Li, Yuan Yao, Lan-Zhe Guo, Xiaoxing Ma, and Yu-Feng Li. Bridging internal probability and self-consistency for effective and efficient llm reasoning. arXiv preprint arXiv:2502.00511, 2025. 4 — [arXiv:2502.00511](https://arxiv.org/abs/2502.00511) find
  247. Jiace Zhu, Yingtao Shen, Jie Zhao, and An Zou. Path-consistency: Prefix enhancement for efficient inference in llm. arXiv preprint arXiv:2409.01281, 2024. 4 — [arXiv:2409.01281](https://arxiv.org/abs/2409.01281) find
  248. Xunyu Zhu, Jian Li, Can Ma, and Weiping Wang. Improving mathematical reasoning capabilities of small language models via feedback-driven distillation. arXiv preprint arXiv:2411.14698, 2024. 4, 17 — [arXiv:2411.14698](https://arxiv.org/abs/2411.14698) find
  249. Ren Zhuang, Ben Wang, and Shuifa Sun. Accelerating chain-of-thought reasoning: When goal-gradient importance meets dynamic skipping. arXiv preprint arXiv:2505.08392, 2025. 4 — [arXiv:2505.08392](https://arxiv.org/abs/2505.08392) find
  250. Xialie Zhuang, Peixian Ma, Zhikai Jia, Shiwei Liu, and Zheng Cao. A technical study into 0.5b reasoning language models, 2025. 4 find