Elamur Research⌕
☰
home / papers / 2025 / Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models

Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models

Paper · 2025 · Rui Wang, Hongru Wang, Boyang Xue et al.

Summary

Large Reasoning Models such as OpenAI o1, DeepSeek R1 and QwQ solve hard math and coding problems by producing long chains of thought. They tend to spend the same heavy effort on every query. As a result, they waste tokens on easy inputs (overthinking) and sometimes stop too early on hard ones (underthinking). This survey calls the goal of matching compute to task difficulty the "reasoning economy," the best balance between accuracy gained and inference cost paid.

The authors organise the literature along the model lifecycle:

  • Causes of inefficiency: data bias and reward hacking.
  • Post-training ("supply side"): methods that build efficiency into the model through data, training algorithms and architecture.
  • Inference ("demand side"): methods that allocate compute adaptively per query.

The survey also covers multimodal and agentic settings, efficiency benchmarks and interpretability. Its main conclusion is that strong post-training and adaptive test-time control must be combined to approach peak performance at minimal cost. This matters to anyone deploying reasoning LLMs under budget or latency limits.

Key points

  • Challenge taxonomy: overly cautious reasoning, superficial reasoning, poor choice of decoding algorithm, and mismatched compute allocation at test time.
  • Post-training remedies: curated reasoning data; Long2short RL (separating quality from length, length penalties, process rewards); budget-aware tuning; explicit and implicit CoT compression; recurrent layers and dynamic-depth architectures.
  • Evaluation resources: DNA Bench shows LRMs can emit up to 70× more unnecessary tokens. DUMB500 (from ThoughtTerminator) tests calibration on trivially easy problems. A dataset built from NPR Sunday Puzzle Challenges probes reasoning that needs only general knowledge.
  • Extensions: multimodal CoT efficiency metrics (relevance rate, reflection quality) and the LaRMA framework, which compares LRMs with ordinary LLMs as agents.
  • Paper list: maintained at github.com/DevoAllen/Awesome-Reasoning-Economy-Papers.

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

🎓University of EdinburghEdinburgh · United Kingdom359 works · 77,352 citations🎓University of Illinois Urbana-ChampaignUrbana · United States184 works · 69,372 citations🎓Princeton UniversityPrinceton · United States90 works · 39,109 citations🎓University of MacauMacao · China33 works · 2,223 citations🎓Southern University of Science and TechnologyShenzhen · China19 works · 1,337 citations

 Fields, topics and concepts

📚 Shelves 5The 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 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 works

Cite

Cite as Rui Wang, Hongru Wang, Boyang Xue, Yixia Li, Jianhui Pang, Shudong Liu, Yi Chen, Jiahao Qiu, Derek Fai Wong, Guanhua Chen, Heng Ji and Kam-Fai Wong. 2025. Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models. arXiv:2503.24377.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2503.24377 · arXiv 2503.24377

@misc{wang2025harnessing,
  title = {{Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models}},
  author = {Rui Wang, Hongru Wang, Boyang Xue, Yixia Li, Jianhui Pang, Shudong Liu, Yi Chen, Jiahao Qiu, Derek Fai Wong, Guanhua Chen, Heng Ji and Kam-Fai Wong},
  year = {2025},
  doi = {10.48550/arxiv.2503.24377},
  eprint = {2503.24377},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2503.24377}
}

References

244 references, 89 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023).
  2. Aradhye Agarwal, Ayan Sengupta, and Tanmoy Chakraborty. 2025. The Art of Scaling Test-Time Compute for Large Language Models. arXiv:2512.02008 [cs.CL] https://arxiv.org/abs/2512.02008 Manuscript submitted to ACM — [arXiv:2512.02008](https://arxiv.org/abs/2512.02008) ↗
  3. Pranjal Aggarwal, Aman Madaan, Yiming Yang, and Mausam. 2023. Let‘s Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Ass… find
  4. Pranjal Aggarwal and Sean Welleck. 2025. L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning. doi:10.48550/ arXiv.2503.04697 arXiv:2503.04697 [cs].
  5. Pranjal Aggarwal and Sean Welleck. 2025. L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning. In Second Conference on Language Modeling. https://openreview.net/forum?id=4jdIxXBNve ↗
  6. AIME. 2025. Art of Problem Solving. https://artofproblemsolving.com/wiki/index.php/AIME_Problems_and_Solutions ↗
  7. Salaheddin Alzubi, Creston Brooks, Purva Chiniya, Edoardo Contente, Chiara von Gerlach, Lucas Irwin, Yihan Jiang, Arda Kaz, Windsor Nguyen, Sewoong Oh, et al. 2025. Open Deep Search: Democratizing Search with Open-source Reasoning Agents. arXiv preprint arXiv:2503.20201 (2025). — [arXiv:2503.20201](https://arxiv.org/abs/2503.20201) find
  8. Carolyn Jane Anderson, Joydeep Biswas, Aleksander Boruch-Gruszecki, Federico Cassano, Molly Q Feldman, Arjun Guha, Francesca Lucchetti, and Zixuan Wu. 2025. PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models. arXiv:2502.01584 [cs.AI] https://arxiv.org/abs/2502.01584 — [arXiv:2502.01584](https://arxiv.org/abs/2502.01584) ↗
  9. Zachary Ankner, Mansheej Paul, Brandon Cui, Jonathan D. Chang, and Prithviraj Ammanabrolu. 2024. Critique-out-Loud Reward Models. arXiv:2408.11791 [cs.LG] https://arxiv.org/abs/2408.11791 ↗
  10. Anthropic. 2025. Circuit Tracing: Revealing Computational Graphs in Language Models. https://transformer-circuits.pub/2025/attribution-graphs/methods.html ↗
  11. Anthropic. 2025. On the Biology of a Large Language Model. https://transformer-circuits.pub/2025/attribution-graphs/biology.html ↗
  12. Gregor Bachmann, Sotiris Anagnostidis, Albert Pumarola, Markos Georgopoulos, Artsiom Sanakoyeu, Yuming Du, Edgar Schönfeld, Ali Thabet, and Jonas Kohler. 2025. Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment. arXiv preprint arXiv:2501.19309 (2025). find
  13. Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Sh… find
  14. Marthe Ballon, Andres Algaba, and Vincent Ginis. 2025. The Relationship Between Reasoning and Performance in Large Language Models – o3 (mini) Thinks Harder, Not Longer. doi:10.48550/arXiv.2502.15631 arXiv:2502.15631 [cs]. — [doi:10.48550/arxiv.2502.15631](https://doi.org/10.48550/arxiv.2502.15631) find
  15. Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, and Azalia Mirhoseini. 2024. Large Language Monkeys: Scaling Inference Compute with Repeated Sampling. arXiv:2407.21787 [cs.LG] https://arxiv.org/abs/2407.21787 ↗
  16. Chao Chen, Kai Liu, Ze Chen, Yi Gu, Yue Wu, Mingyuan Tao, Zhihang Fu, and Jieping Ye. 2024. INSIDE: LLMs’ Internal States Retain the Power of Hallucination Detection. doi:10.48550/arXiv.2402.03744 arXiv:2402.03744. find
  17. Jieneng Chen, Qihang Yu, Xiaohui Shen, Alan Yuille, and Liang-Chieh Chen. 2024. Vitamin: Designing scalable vision models in the vision-language era. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12954–12966. find
  18. Justin Chih-Yao Chen, Archiki Prasad, Swarnadeep Saha, Elias Stengel-Eskin, and Mohit Bansal. 2024. MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning. arXiv:2409.12147 [cs.CL] https://arxiv.org/abs/2409.12147 ↗
  19. Lingjiao Chen, Jared Quincy Davis, Boris Hanin, Peter Bailis, Ion Stoica, Matei Zaharia, and James Zou. 2024. Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems. doi:10.48550/arXiv.2403.02419 arXiv:2403.02419. find
  20. Lichang Chen, Chen Zhu, Jiuhai Chen, Davit Soselia, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, and Bryan Catanzaro. 2024. ODIN: Disentangled Reward Mitigates Hacking in RLHF. In Forty-first International Conference on Machine Learning. https://openreview.net/ forum?id=zcIV8OQFVF ↗
  21. Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, and Wanxiang Che. 2025. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models. arXiv:2503.09567 [cs.AI] https://arxiv.org/abs/2503.09567 ↗
  22. 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. https://openreview.net/forum?id=pC44UMwy2v ↗
  23. Weizhe Chen, Zhicheng Zhang, Guanlin Liu, Renjie Zheng, Wenlei Shi, Chen Dun, Zheng Wu, Xing Jin, and Lin Yan. 2024. Flaming-hot Initiation with Regular Execution Sampling for Large Language Models. doi:10.48550/arXiv.2410.21236 arXiv:2410.21236. find
  24. 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 o1-Like LLMs. arXiv:2412.21187 [cs.CL] https://arxiv.org/abs/241… ↗
  25. Xingwu Chen and Difan Zou. 2024. What can transformer learn with varying depth? case studies on sequence learning tasks. In Proceedings of the 41st International Conference on Machine Learning (Vienna, Austria) (ICML’24). JMLR.org, Article 314, 30 pages. find
  26. Zhixun Chen, Ming Li, Yuxuan Huang, Yali Du, Meng Fang, and Tianyi Zhou. 2025. ATLaS: Agent Tuning via Learning Critical Steps. arXiv preprint arXiv:2503.02197 (2025). Manuscript submitted to ACM find
  27. Jeffrey Cheng and Benjamin Van Durme. 2024. Compressed Chain of Thought: Efficient Reasoning Through Dense Representations. arXiv:2412.13171 [cs.CL] https://arxiv.org/abs/2412.13171 ↗
  28. Tianzhe Chu, Yuexiang Zhai, Jihan Yang, Shengbang Tong, Saining Xie, Dale Schuurmans, Quoc V. Le, Sergey Levine, and Yi Ma. 2025. SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training. doi:10.48550/arXiv.2501.17161 arXiv:2501.17161 [cs].
  29. Xiangxiang Chu, Limeng Qiao, Xinyang Lin, Shuang Xu, Yang Yang, Yiming Hu, Fei Wei, Xinyu Zhang, Bo Zhang, Xiaolin Wei, et al. 2023. Mobilevlm: A fast, strong and open vision language assistant for mobile devices. arXiv preprint arXiv:2312.16886 (2023). find
  30. 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. arXiv:2110.14168 [cs.LG] https://arxiv.org/abs/2110.14168 ↗
  31. 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. 2025. The Danger of Overthinking: Examining the Reasoning-Acti… find
  32. Yingqian Cui, Pengfei He, Jingying Zeng, Hui Liu, Xianfeng Tang, Zhenwei Dai, Yan Han, Chen Luo, Jing Huang, Zhen Li, Suhang Wang, Yue Xing, Jiliang Tang, and Qi He. 2025. Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models. arXiv:2502.13260 [cs.CL… find
  33. DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, and Xiao et al. Bi. 2025. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. doi:10.48550/arXiv.2501.12948 arXiv:2501.12948 [cs].
  34. Yuntian Deng, Yejin Choi, and Stuart Shieber. 2024. From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step. arXiv:2405.14838 [cs.CL] https://arxiv.org/abs/2405.14838 ↗
  35. Yuntian Deng, Kiran Prasad, Roland Fernandez, Paul Smolensky, Vishrav Chaudhary, and Stuart Shieber. 2023. Implicit Chain of Thought Reasoning via Knowledge Distillation. arXiv:2311.01460 [cs.CL] https://arxiv.org/abs/2311.01460 ↗
  36. Shehzaad Dhuliawala, Ilia Kulikov, Ping Yu, Asli Celikyilmaz, Jason Weston, Sainbayar Sukhbaatar, and Jack Lanchantin. 2024. Adaptive Decoding via Latent Preference Optimization. http://arxiv.org/abs/2411.09661 arXiv:2411.09661 version: 1. — [arXiv:2411.09661](https://arxiv.org/abs/2411.09661) ↗
  37. Raul Cavalcante Dinardi, Bruno Yamamoto, Anna Helena Reali Costa, and Artur Jordao. 2025. The Virtues of Brevity: Avoid Overthinking in Parallel Test-Time Reasoning. In NeurIPS 2025 Workshop on Efficient Reasoning. https://openreview.net/forum?id=giq139C56f ↗
  38. Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim, Subhabrata Mukherjee, Victor Ruhle, Laks V. S. Lakshmanan, and Ahmed Hassan Awadallah. 2024. Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing. doi:10.48550/arXiv.2404.14618 find
  39. Alexander R Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021. Summeval: Re-evaluating summarization evaluation. Transactions of the Association for Computational Linguistics 9 (2021), 391–409. find
  40. Siqi Fan, Xin Jiang, Xiang Li, Xuying Meng, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang, and Zhongyuan Wang. 2024. Not All Layers of LLMs Are Necessary During Inference. arXiv:2403.02181 [cs.CL] https://arxiv.org/abs/2403.02181 — [arXiv:2403.02181](https://arxiv.org/abs/2403.02181) ↗
  41. Ying Fan, Yilun Du, Kannan Ramchandran, and Kangwook Lee. 2025. Looped Transformers for Length Generalization. In The Thirteenth International Conference on Learning Representations. https://openreview.net/forum?id=2edigk8yoU ↗
  42. Mehdi Fatemi, Banafsheh Rafiee, Mingjie Tang, and Kartik Talamadupula. 2025. Concise Reasoning via Reinforcement Learning. arXiv:2504.05185 [cs.CL] https://arxiv.org/abs/2504.05185 — [arXiv:2504.05185](https://arxiv.org/abs/2504.05185) ↗
  43. Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, and Liwei Wang. 2023. Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective. In Thirty-seventh Conference on Neural Information Processing Systems. https://openreview.net/forum?id=qHrADgAdYu ↗
  44. Kaituo Feng, Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang, Tianshuo Peng, Benyou Wang, and Xiangyu Yue. 2025. Video-R1: Reinforcing Video Reasoning in MLLMs. arXiv preprint arXiv:2503.21776 (2025). — [arXiv:2503.21776](https://arxiv.org/abs/2503.21776) find
  45. Sicheng Feng, Gongfan Fang, Xinyin Ma, and Xinchao Wang. 2025. Efficient Reasoning Models: A Survey. arXiv:2504.10903 [cs.CL] https://arxiv.org/abs/2504.10903 ↗
  46. Yunlong Feng, Yang Xu, Libo Qin, Yasheng Wang, and Wanxiang Che. 2024. Improving Language Model Reasoning with Self-motivated Learning. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Nicoletta Calzolari, M… find
  47. Leo Gao, John Schulman, and Jacob Hilton. 2023. Scaling laws for reward model overoptimization. In Proceedings of the 40th International Conference on Machine Learning (Honolulu, Hawaii, USA) (ICML’23). JMLR.org, Article 437, 32 pages.
  48. Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu. 2024. Scaling and evaluating sparse autoencoders. doi:10.48550/arXiv.2406.04093 arXiv:2406.04093 [cs]. find
  49. Shanghua Gao, Richard Zhu, Zhenglun Kong, Ayush Noori, Xiaorui Su, Curtis Ginder, Theodoros Tsiligkaridis, and Marinka Zitnik. 2025. TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools. arXiv preprint arXiv:2503.10970 (2025). find
  50. Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi, Stefanie Jegelka, and Sanjiv Kumar. 2024. Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? arXiv:2410.08292 [cs.LG] https://arxiv.org/abs/2410.08292 — [arXiv:2410.08292](https://arxiv.org/abs/2410.08292) ↗
  51. Xuyang Ge, Fukang Zhu, Wentao Shu, Junxuan Wang, Zhengfu He, and Xipeng Qiu. 2024. Automatically Identifying Local and Global Circuits with Linear Computation Graphs. doi:10.48550/arXiv.2405.13868 arXiv:2405.13868 [cs]. Manuscript submitted to ACM — [doi:10.48550/arxiv.2405.13868](https://doi.org/10.48550/arxiv.2405.13868) find
  52. Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, and Tom Goldstein. 2025. Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach. arXiv:2502.05171 [cs.LG] https://arxiv.org/abs/2502.05171 ↗
  53. Angeliki Giannou, Shashank Rajput, Jy-Yong Sohn, Kangwook Lee, Jason D. Lee, and Dimitris Papailiopoulos. 2023. Looped Transformers as Programmable Computers. In Proceedings of the 40th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 202), Andreas Krause,… find
  54. Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, and Weizhu Chen. [n. d.]. CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing. ([n. d.]).
  55. Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition. 6904–6913.
  56. Ryan Greenblatt, Carson Denison, Benjamin Wright, Fabien Roger, Monte MacDiarmid, Sam Marks, Johannes Treutlein, Tim Belonax, Jack Chen, David Duvenaud, Akbir Khan, Julian Michael, Sören Mindermann, Ethan Perez, Linda Petrini, Jonathan Uesato, Jared Kaplan, Buck Shlegeris, Samuel R. Bowman, and Evan… find
  57. Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, et al. 2023. Reinforced self-training (rest) for language modeling. arXiv preprint arXiv:2308.08998 (2023).
  58. Jiani Guo, Zuchao Li, Jie Wu, Qianren Wang, Yun Li, Lefei Zhang, Hai Zhao, and Yujiu Yang. 2025. ToM: Leveraging Tree-oriented MapReduce for Long-Context Reasoning in Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Christos Christodou… find
  59. Zonghao Guo, Ruyi Xu, Yuan Yao, Junbo Cui, Zanlin Ni, Chunjiang Ge, Tat-Seng Chua, Zhiyuan Liu, and Gao Huang. 2024. Llava-uhd: an lmm perceiving any aspect ratio and high-resolution images. In European Conference on Computer Vision. Springer, 390–406. find
  60. Tingxu Han, Zhenting Wang, Chunrong Fang, Shiyu Zhao, Shiqing Ma, and Zhenyu Chen. 2025. Token-Budget-Aware LLM Reasoning. doi:10. 48550/arXiv.2412.18547 arXiv:2412.18547 [cs].
  61. Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian. 2024. Training Large Language Models to Reason in a Continuous Latent Space. arXiv:2412.06769 [cs.CL] https://arxiv.org/abs/2412.06769 ↗
  62. Masoud Hashemi, Oluwanifemi Bamgbose, Sathwik Tejaswi Madhusudhan, Jishnu Sethumadhavan Nair, Aman Tiwari, and Vikas Yadav. 2025. DNA Bench: When Silence is Smarter – Benchmarking Over-Reasoning in Reasoning LLMs. doi:10.48550/arXiv.2503.15793 arXiv:2503.15793 [cs]. — [doi:10.48550/arxiv.2503.15793](https://doi.org/10.48550/arxiv.2503.15793) find
  63. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  64. Andreas Hochlehnert, Hardik Bhatnagar, Vishaal Udandarao, Samuel Albanie, Ameya Prabhu, and Matthias Bethge. 2025. A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility. arXiv:2504.07086 [cs.LG] https://arxiv.org/abs/2504.07086 ↗
  65. Chengsong Huang, Langlin Huang, Jixuan Leng, Jiacheng Liu, and Jiaxin Huang. 2025. Efficient Test-Time Scaling via Self-Calibration. doi:10. 48550/arXiv.2503.00031 arXiv:2503.00031 [cs]. — [arXiv:2503.00031](https://arxiv.org/abs/2503.00031) find
  66. Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. 2023. Large Language Models Cannot Self-Correct Reasoning Yet. https://openreview.net/forum?id=IkmD3fKBPQ ↗
  67. Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. 2024. Large Language Models Cannot Self-Correct Reasoning Yet. In The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id= IkmD3fKBPQ ↗
  68. Jiaxin Huang, Shixiang Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han. 2023. Large Language Models Can Self-Improve. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computatio…
  69. Shijue Huang, Hongru Wang, Wanjun Zhong, Zhaochen Su, Jiazhan Feng, Bowen Cao, and Yi R. Fung. 2025. AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgeting. arXiv:2505.18822 [cs.AI] https://arxiv.org/abs/2505.18822 — [arXiv:2505.18822](https://arxiv.org/abs/2505.18822) ↗
  70. Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Yao Hu, and Shaohui Lin. 2025. Vision-r1: Incentivizing reasoning capability in multimodal large language models. arXiv preprint arXiv:2503.06749 (2025). find
  71. Yupan Huang, Zaiqiao Meng, Fangyu Liu, Yixuan Su, Nigel Collier, and Yutong Lu. 2023. Sparkles: Unlocking chats across multiple images for multimodal instruction-following models. arXiv preprint arXiv:2308.16463 (2023). — [arXiv:2308.16463](https://arxiv.org/abs/2308.16463) find
  72. Drew A Hudson and Christopher D Manning. 2019. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 6700–6709.
  73. Dongzhi Jiang, Renrui Zhang, Ziyu Guo, Yanwei Li, Yu Qi, Xinyan Chen, Liuhui Wang, Jianhan Jin, Claire Guo, Shen Yan, et al. 2025. MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency. arXiv preprint arXiv:2502.09621 (2025). — [arXiv:2502.09621](https://arxiv.org/abs/2502.09621) find
  74. Lingjie Jiang, Xun Wu, Shaohan Huang, Qingxiu Dong, Zewen Chi, Li Dong, Xingxing Zhang, Tengchao Lv, Lei Cui, and Furu Wei. 2025. Think Only When You Need with Large Hybrid-Reasoning Models. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. Manuscript submitted to ACM h… find
  75. Bowen Jin, Hansi Zeng, Zhenrui Yue, Dong Wang, Hamed Zamani, and Jiawei Han. 2025. Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning. arXiv preprint arXiv:2503.09516 (2025).
  76. Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, and Mengnan Du. 2024. The Impact of Reasoning Step Length on Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2024, Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Asso…
  77. Yizhang Jin, Jian Li, Yexin Liu, Tianjun Gu, Kai Wu, Zhengkai Jiang, Muyang He, Bo Zhao, Xin Tan, Zhenye Gan, et al. 2024. Efficient multimodal large language models: A survey. arXiv preprint arXiv:2405.10739 (2024). — [arXiv:2405.10739](https://arxiv.org/abs/2405.10739)
  78. Ryo Kamoi, Yusen Zhang, Nan Zhang, Jiawei Han, and Rui Zhang. 2024. When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs. Transactions of the Association for Computational Linguistics 12 (2024), 1417–1440. doi:10.1162/tacl_a_00713
  79. Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, et al. 2023. Dspy: Compiling declarative language model calls into self-improving pipelines. arXiv preprint arXiv:2310.03714 (2023).
  80. Bo-Kyeong Kim, Geonmin Kim, Tae-Ho Kim, Thibault Castells, Shinkook Choi, Junho Shin, and Hyoung-Kyu Song. 2024. Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods. arXiv:2402.02834 [cs.LG] https://arxiv.org/abs/2402.02834 — [arXiv:2402.02834](https://arxiv.org/abs/2402.02834) ↗
  81. Jeonghye Kim, Sojeong Rhee, Minbeom Kim, Dohyung Kim, Sangmook Lee, Youngchul Sung, and Kyomin Jung. 2025. ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflection. arXiv:2505.15182 [cs.CL] https://arxiv.org/abs/2505.15182 ↗
  82. Sehoon Kim, Karttikeya Mangalam, Suhong Moon, Jitendra Malik, Michael W Mahoney, Amir Gholami, and Kurt Keutzer. 2023. Speculative decoding with big little decoder. Advances in Neural Information Processing Systems 36 (2023), 39236–39256. find
  83. Angang Kimi Team, and Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, Chuning Tang, Congcong Wang, Dehao Zhang, Enming Yuan, Enzhe Lu, Fengxiang Tang, Flood Sung, Guangda Wei, Guokun Lai, Haiqing Guo, Han Zhu, Hao Ding, Hao Hu, Hao Yang, H… — Kimi k1.5: Scaling Reinforcement Learning with LLMs
  84. Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D. Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M. Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal Behbahani, and Aleksandra Faust. 2024. Training Language M…
  85. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natural…
  86. Ayeong Lee, Ethan Che, and Tianyi Peng. 2025. How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach. doi:10.48550/arXiv.2503.01141 arXiv:2503.01141 [cs]. — [doi:10.48550/arxiv.2503.01141](https://doi.org/10.48550/arxiv.2503.01141) find
  87. Yaniv Leviathan, Matan Kalman, and Yossi Matias. 2023. Fast inference from transformers via speculative decoding. In International Conference on Machine Learning. PMLR, 19274–19286. find
  88. Mosh Levy, Alon Jacoby, and Yoav Goldberg. 2024. Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Lun-Wei Ku, Andre Martins, an…
  89. Mosh Levy, Alon Jacoby, and Yoav Goldberg. 2024. Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Lun-Wei Ku, Andre Martins, an…
  90. Gang Li, Yan Chen, Ming Lin, and Tianbao Yang. 2025. DRPO: Efficient Reasoning via Decoupled Reward Policy Optimization. arXiv:2510.04474 [cs.AI] https://arxiv.org/abs/2510.04474 — [arXiv:2510.04474](https://arxiv.org/abs/2510.04474) ↗
  91. Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning. PMLR, 19730–19742.
  92. Junyan Li, Wenshuo Zhao, Yang Zhang, and Chuang Gan. 2025. Steering LLM Thinking with Budget Guidance. arXiv:2506.13752 [cs.CL] https://arxiv.org/abs/2506.13752 ↗
  93. Siheng Li, Cheng Yang, Zesen Cheng, Lemao Liu, Mo Yu, Yujiu Yang, and Wai Lam. 2024. Large Language Models Can Self-Improve in Long-context Reasoning. arXiv preprint arXiv:2411.08147 (2024). Manuscript submitted to ACM — [arXiv:2411.08147](https://arxiv.org/abs/2411.08147) find
  94. Xuefeng Li, Haoyang Zou, and Pengfei Liu. 2025. LIMR: Less is More for RL Scaling. arXiv:2502.11886 [cs.LG] https://arxiv.org/abs/2502.11886 ↗
  95. Yanjing Li, Sheng Xu, Baochang Zhang, Xianbin Cao, Peng Gao, and Guodong Guo. 2022. Q-vit: Accurate and fully quantized low-bit vision transformer. Advances in neural information processing systems 35 (2022), 34451–34463. find
  96. Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan, Xinglin Wang, Bin Sun, Heda Wang, and Kan Li. 2024. Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning. arXiv:2401.10480 [cs.CL] https://arxiv.org/abs/2401.10480 ↗
  97. Zheng Li, Qingxiu Dong, Jingyuan Ma, Di Zhang, Kai Jia, and Zhifang Sui. 2026. SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning. arXiv:2505.11274 [cs.AI] https://arxiv.org/abs/2505.11274 ↗
  98. Zhong-Zhi Li, Duzhen Zhang, Ming-Liang Zhang, Jiaxin Zhang, Zengyan Liu, Yuxuan Yao, Haotian Xu, Junhao Zheng, Pei-Jie Wang, Xiuyi Chen, Yingying Zhang, Fei Yin, Jiahua Dong, Zhijiang Guo, Le Song, and Cheng-Lin Liu. 2025. From System 1 to System 2: A Survey of Reasoning Large Language Models. doi:1…
  99. Tian Liang, Wenxiang Jiao, Zhiwei He, Jiahao Xu, Haitao Mi, and Dong Yu. 2025. DeepCompress: A Dual Reward Strategy for Dynamically Exploring and Compressing Reasoning Chains. arXiv:2510.27419 [cs.AI] https://arxiv.org/abs/2510.27419 — [arXiv:2510.27419](https://arxiv.org/abs/2510.27419) ↗
  100. Baohao Liao, Yuhui Xu, Hanze Dong, Junnan Li, Christof Monz, Silvio Savarese, Doyen Sahoo, and Caiming Xiong. 2025. Reward-Guided Speculative Decoding for Efficient LLM Reasoning. arXiv:2501.19324 [cs.CL] https://arxiv.org/abs/2501.19324 — [arXiv:2501.19324](https://arxiv.org/abs/2501.19324) ↗
  101. Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2023. Let’s Verify Step by Step. http://arxiv.org/abs/2305.20050 arXiv:2305.20050. ↗
  102. Bin Lin, Zhenyu Tang, Yang Ye, Jiaxi Cui, Bin Zhu, Peng Jin, Jinfa Huang, Junwu Zhang, Yatian Pang, Munan Ning, et al. 2024. Moe-llava: Mixture of experts for large vision-language models. arXiv preprint arXiv:2401.15947 (2024). — [arXiv:2401.15947](https://arxiv.org/abs/2401.15947) find
  103. Bill Yuchen Lin, Yicheng Fu, Karina Yang, Faeze Brahman, Shiyu Huang, Chandra Bhagavatula, Prithviraj Ammanabrolu, Yejin Choi, and Xiang Ren. 2023. Swiftsage: A generative agent with fast and slow thinking for complex interactive tasks. Advances in Neural Information Processing Systems 36 (2023), 23…
  104. Zhihang Lin, Mingbao Lin, Luxi Lin, and Rongrong Ji. 2024. Boosting multimodal large language models with visual tokens withdrawal for rapid inference. arXiv preprint arXiv:2405.05803 (2024). — [arXiv:2405.05803](https://arxiv.org/abs/2405.05803) find
  105. Zehui Ling, Deshu Chen, Hongwei Zhang, Yifeng Jiao, Xin Guo, and Yuan Cheng. 2025. Fast on the Easy, Deep on the Hard: Efficient Reasoning via Powered Length Penalty. arXiv:2506.10446 [cs.CL] https://arxiv.org/abs/2506.10446 — [arXiv:2506.10446](https://arxiv.org/abs/2506.10446) ↗
  106. Jiacheng Liu, Andrew Cohen, Ramakanth Pasunuru, Yejin Choi, Hannaneh Hajishirzi, and Asli Celikyilmaz. 2024. Don’t throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding. arXiv:2309.15028 [cs.CL] https://arxiv.org/abs/ 2309.15028 ↗
  107. Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024. Lost in the Middle: How Language Models Use Long Contexts. Transactions of the Association for Computational Linguistics 12 (2024), 157–173. doi:10.1162/tacl_a_00638 Place: Cambridge, M…
  108. Shudong Liu, Zhaocong Li, Xuebo Liu, Runzhe Zhan, Derek F. Wong, Lidia S. Chao, and Min Zhang. 2024. Can LLMs Learn Uncertainty on Their Own? Expressing Uncertainty Effectively in A Self-Training Manner. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Assoc… find
  109. Xin Liu and Lu Wang. 2025. Answer Convergence as a Signal for Early Stopping in Reasoning. arXiv:2506.02536 [cs.CL] https://arxiv.org/abs/ 2506.02536 ↗
  110. Junru Lu, Jiazheng Li, Siyu An, Meng Zhao, Yulan He, Di Yin, and Xing Sun. 2024. Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Yaser Al-Onaizan, Mohit Ban… find
  111. Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. 2024. MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts. In International Conference on Learning Representations (ICLR).
  112. Liangchen Luo, Yinxiao Liu, Rosanne Liu, Samrat Phatale, Harsh Lara, Yunxuan Li, Lei Shu, Yun Zhu, Lei Meng, Jiao Sun, et al. 2024. Improve mathematical reasoning in language models by automated process supervision. arXiv preprint arXiv:2406.06592 2 (2024).
  113. Trung Quoc Luong, Xinbo Zhang, Zhanming Jie, Peng Sun, Xiaoran Jin, and Hang Li. 2024. ReFT: Reasoning with Reinforced Fine-Tuning. arXiv:2401.08967 [cs.CL] https://arxiv.org/abs/2401.08967 ↗
  114. Shangke Lyu, Linjuan Wu, Yuchen Yan, Xingyu Wu, Hao Li, Yongliang Shen, Peisheng Jiang, Weiming Lu, Jun Xiao, and Yueting Zhuang. 2025. Hierarchical Budget Policy Optimization for Adaptive Reasoning. arXiv:2507.15844 [cs.AI] https://arxiv.org/abs/2507.15844 — [arXiv:2507.15844](https://arxiv.org/abs/2507.15844) ↗
  115. Xinyin Ma, Gongfan Fang, and Xinchao Wang. 2023. LLM-Pruner: On the Structural Pruning of Large Language Models. arXiv:2305.11627 [cs.CL] https://arxiv.org/abs/2305.11627 ↗
  116. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. 2023. SELF-REFINE: iterative refinement with… find
  117. Rohin Manvi, Anikait Singh, and Stefano Ermon. 2024. Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation. arXiv:2410.02725 [cs.CL] https://arxiv.org/abs/2410.02725 ↗
  118. Minjia Mao, Bowen Yin, Yu Zhu, and Xiao Fang. 2025. Early Stopping Chain-of-thoughts in Large Language Models. arXiv:2509.14004 [cs.CL] https://arxiv.org/abs/2509.14004 — [arXiv:2509.14004](https://arxiv.org/abs/2509.14004) ↗
  119. Xin Men, Mingyu Xu, Qingyu Zhang, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, and Weipeng Chen. 2024. ShortGPT: Layers in Large Language Models are More Redundant Than You Expect. arXiv:2403.03853 [cs.CL] https://arxiv.org/abs/2403.03853 ↗
  120. Fanqing Meng, Lingxiao Du, Zongkai Liu, Zhixiang Zhou, Quanfeng Lu, Daocheng Fu, Botian Shi, Wenhai Wang, Junjun He, Kaipeng Zhang, et al. 2025. MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale Reinforcement Learning. arXiv preprint arXiv:2503.07365 (2025). find
  121. Yu Meng, Mengzhou Xia, and Danqi Chen. 2024. SimPO: Simple Preference Optimization with a Reference-Free Reward. doi:10.48550/arXiv.2405. 14734 arXiv:2405.14734 [cs]. find
  122. Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. 2025. s1: Simple test-time scaling. arXiv:2501.19393 [cs.CL] https://arxiv.org/abs/2501.19393 ↗
  123. Sania Nayab, Giulio Rossolini, Marco Simoni, Andrea Saracino, Giorgio Buttazzo, Nicolamaria Manes, and Fabrizio Giacomelli. 2025. Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost. arXiv:2407.19825 [cs.CL] https://arxiv.org/abs/2407.19825 — [arXiv:2407.19825](https://arxiv.org/abs/2407.19825) ↗
  124. João Paulo Nogueira, Wentao Sun, Alonso Silva, and Laith Zumot. 2025. Certainty-Guided Reasoning in Large Language Models: A Dynamic Thinking Budget Approach. arXiv:2509.07820 [cs.AI] https://arxiv.org/abs/2509.07820 — [arXiv:2509.07820](https://arxiv.org/abs/2509.07820) ↗
  125. Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E. Gonzalez, M Waleed Kadous, and Ion Stoica. 2025. RouteLLM: Learning to Route LLMs with Preference Data. arXiv:2406.18665 [cs.LG] https://arxiv.org/abs/2406.18665 ↗
  126. OpenAI. 2024. OpenAI o1. https://openai.com/o1 ↗
  127. OpenAI. 2025. Deep Research. https://openai.com/index/introducing-deep-research ↗
  128. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022. Train…
  129. Daniele Paliotta, Junxiong Wang, Matteo Pagliardini, Kevin Y Li, Aviv Bick, J Zico Kolter, Albert Gu, François Fleuret, and Tri Dao. 2025. Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners. arXiv preprint arXiv:2502.20339 (2025). — [arXiv:2502.20339](https://arxiv.org/abs/2502.20339) find
  130. Jiabao Pan, Yan Zhang, Chen Zhang, Zuozhu Liu, Hongwei Wang, and Haizhou Li. 2024. DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Yaser Al-Onaizan, Mohit Bansal, and Yu… find
  131. Jianhui Pang, Fanghua Ye, Derek Wong, Xin He, Wanshun Chen, and Longyue Wang. 2024. Anchor-based Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2024, Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Computational Linguistics, Bangkok, Th… find
  132. Jianhui Pang, Fanghua Ye, Derek Fai Wong, Dian Yu, Shuming Shi, Zhaopeng Tu, and Longyue Wang. 2025. Salute the classic: Revisiting challenges of machine translation in the age of large language models. Transactions of the Association for Computational Linguistics 13 (2025), 73–95.
  133. Shubham Parashar, Blake Olson, Sambhav Khurana, Eric Li, Hongyi Ling, James Caverlee, and Shuiwang Ji. 2025. Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights. arXiv:2502.12521 [cs.AI] https://arxiv.org/abs/2502.12521 — [arXiv:2502.12521](https://arxiv.org/abs/2502.12521) ↗
  134. Ryan Park, Rafael Rafailov, Stefano Ermon, and Chelsea Finn. 2024. Disentangling Length from Quality in Direct Preference Optimization. In Findings of the Association for Computational Linguistics: ACL 2024, Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Computational Linguist…
  135. Yingzhe Peng, Gongrui Zhang, Miaosen Zhang, Zhiyuan You, Jie Liu, Qipeng Zhu, Kai Yang, Xingzhong Xu, Xin Geng, and Xu Yang. 2025. LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL. arXiv preprint arXiv:2503.07536 (2025). — [arXiv:2503.07536](https://arxiv.org/abs/2503.07536) find
  136. Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhang, Chen Bo Calvin Zhang, Mohamed Shaaban, John Ling, Sean Shi, et al. 2025. Humanity’s Last Exam. arXiv preprint arXiv:2501.14249 (2025).
  137. Xiao Pu, Michael Saxon, Wenyue Hua, and William Yang Wang. 2025. THOUGHTTERMINATOR: Benchmarking, Calibrating, and Mitigating Overthinking in Reasoning Models. arXiv:2504.13367 [cs.CL] https://arxiv.org/abs/2504.13367 — [arXiv:2504.13367](https://arxiv.org/abs/2504.13367) ↗
  138. Xiao Pu, Michael Saxon, Wenyue Hua, and William Yang Wang. 2025. ThoughtTerminator: Benchmarking, Calibrating, and Mitigating Overthinking in Reasoning Models. In Second Conference on Language Modeling. https://openreview.net/forum?id=oHR862dpMC ↗
  139. Jianing Qi, Xi Ye, Hao Tang, Zhigang Zhu, and Eunsol Choi. 2025. Learning to Reason Across Parallel Samples for LLM Reasoning. In NeurIPS 2025 Workshop on Efficient Reasoning. https://openreview.net/forum?id=lVjZPH9Zyq ↗
  140. Yanyuan Qiao, Zheng Yu, Longteng Guo, Sihan Chen, Zijia Zhao, Mingzhen Sun, Qi Wu, and Jing Liu. 2024. Vl-mamba: Exploring state space models for multimodal learning. arXiv preprint arXiv:2403.13600 (2024). — [arXiv:2403.13600](https://arxiv.org/abs/2403.13600) find
  141. Yiwei Qin, Xuefeng Li, Haoyang Zou, Yixiu Liu, Shijie Xia, Zhen Huang, Yixin Ye, Weizhe Yuan, Hector Liu, Yuanzhi Li, and Pengfei Liu. 2024. O1 Replication Journey: A Strategic Progress Report – Part 1. doi:10.48550/arXiv.2410.18982 arXiv:2410.18982 [cs]. find
  142. Jiahao Qiu, Yifu Lu, Yifan Zeng, Jiacheng Guo, Jiayi Geng, Huazheng Wang, Kaixuan Huang, Yue Wu, and Mengdi Wang. 2024. Treebon: Enhancing inference-time alignment with speculative tree-search and best-of-n sampling. arXiv preprint arXiv:2410.16033 (2024). Manuscript submitted to ACM find
  143. 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…
  144. Yuxiao Qu, Matthew Y. R. Yang, Amrith Setlur, Lewis Tunstall, Edward Emanuel Beeching, Ruslan Salakhutdinov, and Aviral Kumar. 2025. Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning. http://arxiv.org/abs/2503.07572 arXiv:2503.07572. ↗
  145. QwQ. 2025. QwQ-32B: Embracing the Power of Reinforcement Learning \| Qwen. https://qwenlm.github.io/blog/qwq-32b/ ↗
  146. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023. Direct Preference Optimization: Your Language Model is Secretly a Reward Model. In Advances in Neural Information Processing Systems, A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and…
  147. Hossein A. Rahmani, Satyapriya Krishna, Xi Wang, Mohammadmehdi Naghiaei, and Emine Yilmaz. 2025. Self-Correcting Large Language Models: Generation vs. Multiple Choice. arXiv:2511.09381 [cs.CL] https://arxiv.org/abs/2511.09381 — [arXiv:2511.09381](https://arxiv.org/abs/2511.09381) ↗
  148. Shyam Sundhar Ramesh, Yifan Hu, Iason Chaimalas, Viraj Mehta, Pier Giuseppe Sessa, Haitham Bou Ammar, and Ilija Bogunovic. 2024. Group Robust Preference Optimization in Reward-free RLHF. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. https://openreview.net/forum?id=… ↗
  149. Anton Razzhigaev, Matvey Mikhalchuk, Temurbek Rahmatullaev, Elizaveta Goncharova, Polina Druzhinina, Ivan Oseledets, and Andrey Kuznetsov. 2025. LLM-Microscope: Uncovering the Hidden Role of Punctuation in Context Memory of Transformers. doi:10.48550/arXiv.2502.15007 arXiv:2502.15007 [cs]. find
  150. Zhiwen Ruan, Yixia Li, Yefeng Liu, Yun Chen, Weihua Luo, Peng Li, Yang Liu, and Guanhua Chen. 2025. G2: Guided Generation for Enhanced Output Diversity in LLMs. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Christos Christodoulopoulos, Tanmoy Chakraborty,…
  151. Swarnadeep Saha, Archiki Prasad, Justin Chih-Yao Chen, Peter Hase, Elias Stengel-Eskin, and Mohit Bansal. 2024. System-1. x: Learning to balance fast and slow planning with language models. arXiv preprint arXiv:2407.14414 (2024). find
  152. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. arXiv:1707.06347 [cs.LG] find
  153. Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Tran, Yi Tay, and Donald Metzler. 2022. Confident Adaptive Language Modeling. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associ… find
  154. Amrith Setlur, Nived Rajaraman, Sergey Levine, and Aviral Kumar. 2025. Scaling Test-Time Compute Without Verification or RL is Suboptimal. doi:10.48550/arXiv.2502.12118 arXiv:2502.12118 [cs]. find
  155. Xu Shen, Song Wang, Zhen Tan, Laura Yao, Xinyu Zhao, Kaidi Xu, Xin Wang, and Tianlong Chen. 2025. FaithCoT-Bench: Benchmarking Instance-Level Faithfulness of Chain-of-Thought Reasoning. arXiv:2510.04040 [cs.AI] https://arxiv.org/abs/2510.04040 — [arXiv:2510.04040](https://arxiv.org/abs/2510.04040) ↗
  156. Haizhou Shi, Ye Liu, Bo Pang, Zeyu Leo Liu, Hao Wang, Silvio Savarese, Caiming Xiong, Yingbo Zhou, and Semih Yavuz. 2025. SSR: Socratic Self-Refine for Large Language Model Reasoning. arXiv:2511.10621 [cs.CL] https://arxiv.org/abs/2511.10621 — [arXiv:2511.10621](https://arxiv.org/abs/2511.10621) ↗
  157. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik R Narasimhan, and Shunyu Yao. 2023. Reflexion: language agents with verbal reinforcement learning. In Thirty-seventh Conference on Neural Information Processing Systems. https://openreview.net/forum?id=vAElhFcKW6 ↗
  158. Prasann Singhal, Tanya Goyal, Jiacheng Xu, and Greg Durrett. 2024. A Long Way to Go: Investigating Length Correlations in RLHF. In First Conference on Language Modeling. https://openreview.net/forum?id=G8LaO1P0xv ↗
  159. Nishad Singhi, Hritik Bansal, Arian Hosseini, Aditya Grover, Kai-Wei Chang, Marcus Rohrbach, and Anna Rohrbach. 2025. When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning. arXiv:2504.01005 [cs.CL] https://arxiv.org/abs/2504. 01005 ↗
  160. Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, and David Krueger. 2022. Defining and Characterizing Reward Hacking. arXiv:2209.13085 [cs.LG] https://arxiv.org/abs/2209.13085 — [arXiv:2209.13085](https://arxiv.org/abs/2209.13085) ↗
  161. Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. 2024. Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters. doi:10.48550/arXiv.2408.03314 arXiv:2408.03314 [cs].
  162. Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano. 2020. Learning to summarize from human feedback. In Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS ’20). Curran Associat… find
  163. Vighnesh Subramaniam, Yilun Du, Joshua B Tenenbaum, Antonio Torralba, Shuang Li, and Igor Mordatch. 2025. Multiagent finetuning: Self improvement with diverse reasoning chains. arXiv preprint arXiv:2501.05707 (2025). — [arXiv:2501.05707](https://arxiv.org/abs/2501.05707) find
  164. 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. arXiv:2503.16419 [cs.CL] https://arxiv.org/abs/2503.16419 Manusc… ↗
  165. Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao Qiu, Ming Yin, Mengdi Wang, Peter Bartlett, and Andrea Zanette. 2024. Fast Best-of-N Decoding via Speculative Rejection. doi:10.48550/arXiv.2410.20290 arXiv:2410.20290 [cs]. find
  166. Renliang Sun, Wei Cheng, Dawei Li, Haifeng Chen, and Wei Wang. 2025. Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning. arXiv:2510.10103 [cs.CL] https://arxiv.org/abs/2510.10103 ↗
  167. Wenhui Tan, Jiaze Li, Jianzhong Ju, Zhenbo Luo, Jian Luan, and Ruihua Song. 2025. Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains. arXiv:2505.16552 [cs.CL] https://arxiv.org/abs/2505.16552 ↗
  168. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford Alpaca: An Instruction-following LLaMA model. https://github.com/tatsu-lab/stanford_alpaca. ↗
  169. Guiyao Tie, Zenghui Yuan, Zeli Zhao, Chaoran Hu, Tianhe Gu, Ruihang Zhang, Sizhe Zhang, Junran Wu, Xiaoyue Tu, Ming Jin, Qingsong Wen, Lixing Chen, Pan Zhou, and Lichao Sun. 2025. Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMs. arXiv:2510.16062 [cs.CL] https://arxiv.org/abs/2510… — [arXiv:2510.16062](https://arxiv.org/abs/2510.16062) ↗
  170. Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. 2021. Training data-efficient image transformers & distillation through attention. In International conference on machine learning. PMLR, 10347–10357.
  171. Neeraj Varshney, Agneet Chatterjee, Mihir Parmar, and Chitta Baral. 2024. Investigating Acceleration of LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with ‘LITE’. In Findings of the Association for Computational Linguistics: NAACL 2024, Kevin Duh, Helena Gomez, and S… find
  172. Guangya Wan, Yuqi Wu, Jie Chen, and Sheng Li. 2025. Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling. doi:10.48550/arXiv.2408.17017 arXiv:2408.17017 [cs].
  173. Hongru Wang, Deng Cai, Wanjun Zhong, Shijue Huang, Jeff Z Pan, Zeming Liu, and Kam-Fai Wong. 2025. Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalyst. In Workshop on Reasoning and Planning for Large Language Models. find
  174. Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun Zhou, Mingjie Zhan, and Hongsheng Li. 2024. Measuring multimodal mathematical reasoning with math-vision dataset. Advances in Neural Information Processing Systems 37 (2024), 95095–95169.
  175. Peiyi Wang, Lei Li, Zhihong Shao, R. X. Xu, Damai Dai, Yifei Li, Deli Chen, Y. Wu, and Zhifang Sui. 2024. Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations. doi:10.48550/arXiv.2312.08935 arXiv:2312.08935.
  176. Rui Wang, Hongru Wang, Fei Mi, Boyang Xue, Yi Chen, Kam-Fai Wong, and Ruifeng Xu. 2024. Enhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: H… find
  177. Xinglin Wang, Shaoxiong Feng, Yiwei Li, Peiwen Yuan, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, and Kan Li. 2025. Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning. arXiv:2408.13457 [cs.CL] https://arxiv.org/abs/2408.13457 ↗
  178. Xiangqi Wang, Yue Huang, Yanbo Wang, Xiaonan Luo, Kehan Guo, Yujun Zhou, and Xiangliang Zhang. 2025. AdaReasoner: Adaptive Reasoning Enables More Flexible Thinking in Large Language Models. arXiv:2505.17312 [cs.AI] https://arxiv.org/abs/2505.17312 ↗
  179. Xinyuan Wang, Yanchi Liu, Wei Cheng, Xujiang Zhao, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, and Haifeng Chen. 2025. MixLLM: Dynamic Routing in Mixed Large Language Models. arXiv:2502.18482 [cs.CL] https://arxiv.org/abs/2502.18482 ↗
  180. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023. Self-Consistency Improves Chain of Thought Reasoning in Language Models. In The Eleventh International Conference on Learning Representations. https://openreview.net/forum?id=1PL1… ↗
  181. Yiqi Wang, Wentao Chen, Xiaotian Han, Xudong Lin, Haiteng Zhao, Yongfei Liu, Bohan Zhai, Jianbo Yuan, Quanzeng You, and Hongxia Yang. 2024. Exploring the reasoning abilities of multimodal large language models (mllms): A comprehensive survey on emerging trends in multimodal reasoning. arXiv preprint… — [arXiv:2401.06805](https://arxiv.org/abs/2401.06805) find
  182. Yue Wang, Qiuzhi Liu, Jiahao Xu, Tian Liang, Xingyu Chen, Zhiwei He, Linfeng Song, Dian Yu, Juntao Li, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, and Dong Yu. 2025. Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs. doi:10.48550/arXiv.2501.18585 arXiv:2501.18585 [cs]. find
  183. Yixu Wang, Yan Teng, Kexin Huang, Chengqi Lyu, Songyang Zhang, Wenwei Zhang, Xingjun Ma, Yu-Gang Jiang, Yu Qiao, and Yingchun Wang. 2024. Fake Alignment: Are LLMs Really Aligned Well?. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistic… find
  184. Yiping Wang, Qing Yang, Zhiyuan Zeng, Liliang Ren, Liyuan Liu, Baolin Peng, Hao Cheng, Xuehai He, Kuan Wang, Jianfeng Gao, Weizhu Chen, Shuohang Wang, Simon Shaolei Du, and Yelong Shen. 2025. Reinforcement Learning for Reasoning in Large Language Models with One Training Example. arXiv:2504.20571 [c… find
  185. Yiming Wang, Pei Zhang, Siyuan Huang, Baosong Yang, Zhuosheng Zhang, Fei Huang, and Rui Wang. 2025. Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decoding. doi:10.48550/arXiv.2503.01422 arXiv:2503.01422 [cs]. find
  186. Zhexuan Wang, Yutong Wang, Xuebo Liu, Liang Ding, Miao Zhang, Jie Liu, and Min Zhang. 2025. AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration. arXiv preprint arXiv:2503.18891 (2025). Manuscript submitted to ACM
  187. Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021. Finetuned Language Models are Zero-Shot Learners. In International Conference on Learning Representations.
  188. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022. Chain-of-thought prompting elicits reasoning in large language models. In Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS ’22).…
  189. Zihao Wei, Liang Pang, Jiahao Liu, Wenjie Shi, Jingcheng Deng, Shicheng Xu, Zenghao Duan, Fei Sun, Huawei Shen, and Xueqi Cheng. 2026. The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis. arXiv:2508.17627 [cs.CL] https://arxiv.org/abs/2508.17627 ↗
  190. Hao Wen, Xinrui Wu, Yi Sun, Feifei Zhang, Liye Chen, Jie Wang, Yunxin Liu, Yunhao Liu, Ya-Qin Zhang, and Yuanchun Li. 2025. BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens. arXiv:2508.17196 [cs.LG] https://arxiv.org/abs/2508.17196 — [arXiv:2508.17196](https://arxiv.org/abs/2508.17196) ↗
  191. Wanxing Wu, He Zhu, Yixia Li, Lei Yang, Jiehui Zhao, Hongru Wang, Jian Yang, Benyou Wang, Bingyi Jing, and Guanhua Chen. 2026. Towards Fair and Comprehensive Evaluation of Routers in Collaborative LLM Systems. arXiv:2602.11877 [cs.CL] https://arxiv.org/abs/2602.11877 — [arXiv:2602.11877](https://arxiv.org/abs/2602.11877) ↗
  192. Yuyang Wu, Yifei Wang, Tianqi Du, Stefanie Jegelka, and Yisen Wang. 2025. When More is Less: Understanding Chain-of-Thought Length in LLMs. doi:10.48550/arXiv.2502.07266 arXiv:2502.07266 [cs]. find
  193. Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A. Smith, Mari Ostendorf, and Hannaneh Hajishirzi. 2023. Fine-grained human feedback gives better rewards for language model training. In Proceedings of the 37th International Conference on Neural Information Proce…
  194. Heming Xia, Tao Ge, Peiyi Wang, Si-Qing Chen, Furu Wei, and Zhifang Sui. 2023. Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation. In Findings of the Association for Computational Linguistics: EMNLP 2023, Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Assoc…
  195. Heming Xia, Yongqi Li, Chak Tou Leong, Wenjie Wang, and Wenjie Li. 2025. TokenSkip: Controllable Chain-of-Thought Compression in LLMs. arXiv:2502.12067 [cs.CL] https://arxiv.org/abs/2502.12067 ↗
  196. Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, and Zhifang Sui. 2024. Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding. In Findings of the Association for Computational Linguistics: ACL 2024, Lun-Wei Ku,…
  197. Kun Xiang, Zhili Liu, Zihao Jiang, Yunshuang Nie, Kaixin Cai, Yiyang Yin, Runhui Huang, Haoxiang Fan, Hanhui Li, Weiran Huang, et al. 2025. Can Atomic Step Decomposition Enhance the Self-structured Reasoning of Multimodal Large Models? arXiv preprint arXiv:2503.06252 (2025). — [arXiv:2503.06252](https://arxiv.org/abs/2503.06252) find
  198. Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, Xu Zhao, Min-Yen Kan, Junxian He, and Qizhe Xie. 2023. Self-Evaluation Guided Beam Search for Reasoning. arXiv:2305.00633 [cs.CL] https://arxiv.org/abs/2305.00633 ↗
  199. Huajian Xin, Daya Guo, Zhihong Shao, Zhizhou Ren, Qihao Zhu, Bo Liu, Chong Ruan, Wenda Li, and Xiaodan Liang. 2024. DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data. arXiv:2405.14333 [cs.AI] https://arxiv.org/abs/2405.14333 ↗
  200. Ruoxi Xu, Hongyu Lin, Xianpei Han, Jia Zheng, Weixiang Zhou, Le Sun, and Yingfei Sun. 2024. Large Language Models Often Say One Thing and Do Another. https://openreview.net/forum?id=RTHbao4Mib ↗
  201. Xin Xu, Tianhao Chen, Fan Zhang, Wanlong Liu, Pengxiang Li, Ajay Kumar Jaiswal, Yuchen Yan, Jishan Hu, Yang Wang, Hao Chen, Shiwei Liu, Shizhe Diao, Can Yang, and Lu Yin. 2025. Double-Checker: Enhancing Reasoning of Slow-Thinking LLMs via Self-Critical Fine-Tuning. arXiv:2506.21285 [cs.CL] https://a… find
  202. Yige Xu, Xu Guo, Zhiwei Zeng, and Chunyan Miao. 2025. SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs. arXiv:2502.12134 [cs.CL] https://arxiv.org/abs/2502.12134 ↗
  203. Boyang Xue, Fei Mi, Qi Zhu, Hongru Wang, Rui Wang, Sheng Wang, Erxin Yu, Xuming Hu, and Kam-Fai Wong. 2024. UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models. doi:10.48550/arXiv.2412.11803 arXiv:2412.11803 [cs]. find
  204. Kaiwen Yan, Xuanqing Shi, Hongcheng Guo, Wenxuan Wang, Zhuosheng Zhang, and Chengwei Qin. 2025. DRQA: Dynamic Reasoning Quota Allocation for Controlling Overthinking in Reasoning Large Language Models. arXiv:2508.17803 [cs.CL] https://arxiv.org/abs/2508.17803 — [arXiv:2508.17803](https://arxiv.org/abs/2508.17803) ↗
  205. An Yang, Beichen Zhang, Binyuan Hui, Bofei Gao, Bowen Yu, Chengpeng Li, Dayiheng Liu, Jianhong Tu, Jingren Zhou, Junyang Lin, Keming Lu, Mingfeng Xue, Runji Lin, Tianyu Liu, Xingzhang Ren, and Zhenru Zhang. 2024. Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement. a…
  206. Chenxu Yang, Qingyi Si, Yongjie Duan, Zheliang Zhu, Chenyu Zhu, Qiaowei Li, Minghui Chen, Zheng Lin, and Weiping Wang. 2025. Dynamic Early Exit in Reasoning Models. arXiv:2504.15895 [cs.CL] https://arxiv.org/abs/2504.15895 ↗
  207. Kevin Yang, Dan Klein, Asli Celikyilmaz, Nanyun Peng, and Yuandong Tian. 2024. RLCD: Reinforcement Learning from Contrastive Distillation for LM Alignment. In The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=v3XXtxWKi6 ↗
  208. Liu Yang, Kangwook Lee, Robert D Nowak, and Dimitris Papailiopoulos. 2024. Looped Transformers are Better at Learning Learning Algorithms. In The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=HHbRxoDTxE ↗
  209. Ling Yang, Zhaochen Yu, Tianjun Zhang, Shiyi Cao, Minkai Xu, Wentao Zhang, Joseph E. Gonzalez, and Bin CUI. 2024. Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. https://openreview.net/forum?… ↗
  210. Wenkai Yang, Shuming Ma, Yankai Lin, and Furu Wei. 2025. Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning. arXiv:2502.18080 [cs.CL] https://arxiv.org/abs/2502.18080 Manuscript submitted to ACM ↗
  211. Yifei Yang, Zouying Cao, and Hai Zhao. 2024. LaCo: Large Language Model Pruning via Layer Collapse. In Findings of the Association for Computational Linguistics: EMNLP 2024, Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (Eds.). Association for Computational Linguistics, Miami, Florida, USA, 6401… find
  212. Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, Yuxin Song, Haocheng Feng, Li Shen, et al. 2024. Mulberry: Empowering mllm with o1-like reasoning and reflection via collective monte carlo tree search. arXiv preprint arXiv:2412.18319 (2024). — [arXiv:2412.18319](https://arxiv.org/abs/2412.18319) find
  213. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of Thoughts: Deliberate Problem Solving with Large Language Models. doi:10.48550/arXiv.2305.10601 arXiv:2305.10601 [cs]. find
  214. Tian Ye, Zicheng Xu, Yuanzhi Li, and Zeyuan Allen-Zhu. 2025. Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process. In The Thirteenth International Conference on Learning Representations. https://openreview.net/forum?id=Tn5B6Udq3E ↗
  215. Yixin Ye, Zhen Huang, Yang Xiao, Ethan Chern, Shijie Xia, and Pengfei Liu. 2025. LIMO: Less is More for Reasoning. arXiv:2502.03387 [cs.CL] https://arxiv.org/abs/2502.03387 ↗
  216. Edward Yeo, Yuxuan Tong, Morry Niu, Graham Neubig, and Xiang Yue. 2025. Demystifying Long Chain-of-Thought Reasoning in LLMs. doi:10.48550/arXiv.2502.03373 arXiv:2502.03373 [cs].
  217. Fei Yu, Anningzhe Gao, and Benyou Wang. 2024. OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning. In Findings of the Association for Computational Linguistics: NAACL 2024, Kevin Duh, Helena Gomez, and Steven Bethard (Eds.). Association for Computational Linguistics, Mexico C… find
  218. Fang Yu, Kun Huang, Meng Wang, Yuan Cheng, Wei Chu, and Li Cui. 2022. Width & depth pruning for vision transformers. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 3143–3151. find
  219. Ping Yu, Jing Xu, Jason Weston, and Ilia Kulikov. 2024. Distilling system 2 into system 1. arXiv preprint arXiv:2407.06023 (2024). find
  220. Qifan Yu, Zhenyu He, Sijie Li, Xun Zhou, Jun Zhang, Jingjing Xu, and Di He. 2025. Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning. arXiv:2502.08482 [cs.CL] https://arxiv.org/abs/2502.08482 ↗
  221. Weizhe Yuan, Ilia Kulikov, Ping Yu, Kyunghyun Cho, Sainbayar Sukhbaatar, Jason Weston, and Jing Xu. 2024. Following Length Constraints in Instructions. arXiv:2406.17744 [cs.CL] https://arxiv.org/abs/2406.17744 ↗
  222. Linan Yue, Yichao Du, Yizhi Wang, Weibo Gao, Fangzhou Yao, Li Wang, Ye Liu, Ziyu Xu, Qi Liu, Shimin Di, and Min-Ling Zhang. 2025. Don’t Overthink It: A Survey of Efficient R1-style Large Reasoning Models. arXiv:2508.02120 [cs.AI] https://arxiv.org/abs/2508.02120 ↗
  223. Eric Zelikman, Georges Raif Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, and Noah Goodman. 2024. Quiet-star: Language models can teach themselves to think before speaking. In First Conference on Language Modeling.
  224. Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. 2022. Star: Bootstrapping reasoning with reasoning. Advances in Neural Information Processing Systems 35 (2022), 15476–15488.
  225. Yongcheng Zeng, Xinyu Cui, Xuanfa Jin, Qirui Mi, Guoqing Liu, Zexu Sun, Mengyue Yang, Dong Li, Weiyu Ma, Ning Yang, Jian Zhao, Jianye Hao, Haifeng Zhang, and Jun Wang. 2025. Evolving LLMs’ Self-Refinement Capability via Synergistic Training-Inference Optimization. arXiv:2502.05605 [cs.CL] https://ar… — [arXiv:2502.05605](https://arxiv.org/abs/2502.05605) find
  226. Guibin Zhang, Yanwei Yue, Zhixun Li, Sukwon Yun, Guancheng Wan, Kun Wang, Dawei Cheng, Jeffrey Xu Yu, and Tianlong Chen. 2025. Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems. In The Thirteenth International Conference on Learning Representations. https://openrev… find
  227. Jingyi Zhang, Jiaxing Huang, Huanjin Yao, Shunyu Liu, Xikun Zhang, Shijian Lu, and Dacheng Tao. 2025. R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization. arXiv preprint arXiv:2503.12937 (2025). — [arXiv:2503.12937](https://arxiv.org/abs/2503.12937) find
  228. Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, and Ningyu Zhang. 2025. LightThinker: Thinking Step-by-Step Compression. arXiv:2502.15589 [cs.CL] https://arxiv.org/abs/2502.15589 ↗
  229. Yanzhi Zhang, Yitong Duan, Zhaoxi Zhang, Jiyan He, and Shuxin Zheng. 2025. Population-Evolve: a Parallel Sampling and Evolutionary Method for LLM Math Reasoning. arXiv:2512.19081 [cs.AI] https://arxiv.org/abs/2512.19081 — [arXiv:2512.19081](https://arxiv.org/abs/2512.19081) ↗
  230. Zhuosheng Zhang, Aston Zhang, Mu Li, George Karypis, Alex Smola, et al. 2024. Multimodal Chain-of-Thought Reasoning in Language Models. Transactions on Machine Learning Research (2024).
  231. Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. 2025. The lessons of developing process reward models in mathematical reasoning. arXiv preprint arXiv:2501.07301 (2025).
  232. Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. 2025. The Lessons of Developing Process Reward Models in Mathematical Reasoning. arXiv:2501.07301 [cs.CL] https://arxiv.org/abs/2501.07301 ↗
  233. Kai Zhao, Yanjun Zhao, Jiaming Song, Shien He, Lusheng Zhang, Qiang Zhang, and Tianjiao Li. 2025. SABER: Switchable and Balanced Training for Efficient LLM Reasoning. arXiv:2508.10026 [cs.CL] https://arxiv.org/abs/2508.10026 — [arXiv:2508.10026](https://arxiv.org/abs/2508.10026) ↗
  234. Wenhao Zheng, Yixiao Chen, Weitong Zhang, Souvik Kundu, Yun Li, Zhengzhong Liu, Eric P Xing, Hongyi Wang, and Huaxiu Yao. 2025. CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing. arXiv preprint arXiv:2502.01976 (2025). — [arXiv:2502.01976](https://arxiv.org/abs/2502.01976) find
  235. Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan. 2023. AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models. arXiv:2304.06364 [cs.CL] https://arxiv.org/abs/2304.06364 ↗
  236. Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang. 2024. Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models. arXiv:2310.04406 [cs.AI] https://arxiv.org/abs/2310.04406 Manuscript submitted to ACM ↗
  237. Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy. 2023. LIMA: Less Is More for Alignment. doi:10.48550/arXiv.2305.11206 arXiv:2305.11206 [cs]. find
  238. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi. 2023. Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. In The Eleventh International Conference on Learning Representation…
  239. Heng Zhou, Hejia Geng, Xiangyuan Xue, Zhenfei Yin, and Lei Bai. 2025. ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks. arXiv preprint arXiv:2503.02390 (2025). find
  240. Xueyang Zhou, Guiyao Tie, Guowen Zhang, Weidong Wang, Zhigang Zuo, Di Wu, Duanfeng Chu, Pan Zhou, Lichao Sun, and Neil Zhenqiang Gong. 2025. Large Reasoning Models in Agent Scenarios: Exploring the Necessity of Reasoning Capabilities. arXiv preprint arXiv:2503.11074 (2025). — [arXiv:2503.11074](https://arxiv.org/abs/2503.11074) find
  241. Zihao Zhou, Shudong Liu, Maizhen Ning, Wei Liu, Jindong Wang, Derek F. Wong, Xiaowei Huang, Qiufeng Wang, and Kaizhu Huang. 2025. Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklist. In The Thirteenth International Conference on Learning Representations. https… find
  242. Zixuan Zhou, Xuefei Ning, Ke Hong, Tianyu Fu, Jiaming Xu, Shiyao Li, Yuming Lou, Luning Wang, Zhihang Yuan, Xiuhong Li, Shengen Yan, Guohao Dai, Xiao-Ping Zhang, Yuhan Dong, and Yu Wang. 2024. A Survey on Efficient Inference for Large Language Models. arXiv:2404.14294 [cs.CL] https://arxiv.org/abs/2… ↗
  243. Jiace Zhu, Yingtao Shen, Jie Zhao, and An Zou. 2025. Path-Consistency: Prefix Enhancement for Efficient Inference in LLM. doi:10.48550/arXiv. 2409.01281 arXiv:2409.01281 [cs]. — [doi:10.48550/arxiv](https://doi.org/10.48550/arxiv) find
  244. Yichen Zhu, Minjie Zhu, Ning Liu, Zhiyuan Xu, and Yaxin Peng. 2024. Llava-phi: Efficient multi-modal assistant with small language model. In Proceedings of the 1st International Workshop on Efficient Multimedia Computing under Limited. 18–22. Manuscript submitted to ACM find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/wang-2025-harnessing-reasoning-economy-survey-efficient/