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Large language models (LLMs) have demonstrated remarkable reasoning capabilities through test-time scaling approaches, particularly when fine-tuned with chain-of-thought (CoT) data distilled from more powerful large reasoning models (LRMs).
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Speculative decoding with big little decoder
Sehoon Kim, Karttikeya Mangalam, Suhong Moon, Jitendra Malik, Michael W Mahoney, Amir Gholami, and Kurt Keutzer · 2023
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Let’s verify step by step
Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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Are more llm calls all you need? towards the scaling properties of compound ai systems
Lingjiao Chen, Jared Quincy Davis, Boris Hanin, Peter Bailis, Ion Stoica, Matei A Zaharia, and James Y Zou · 2024
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Do not think that much for 2+ 3=? on the overthinking of o1-like llms
Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, et al · 2024
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Break the chain: Large language models can be shortcut reasoners
Mengru Ding, Hanmeng Liu, Zhizhang Fu, Jian Song, Wenbo Xie, and Yue Zhang · 2024
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Efficiently serving llm reasoning programs with certaindex
Yichao Fu, Junda Chen, Siqi Zhu, Zheyu Fu, Zhongdongming Dai, Aurick Qiao, and Hao Zhang · 2024
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Token-budget-aware llm reasoning
Tingxu Han, Zhenting Wang, Chunrong Fang, Shiyu Zhao, Shiqing Ma, and Zhenyu Chen · 2024
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Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al · 2024
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Eagle: Speculative sampling requires rethinking feature uncertainty
Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang · 2024
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Routellm: Learning to route llms with preference data, 2024
Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E Gonzalez, M Waleed Kadous, and Ion Stoica · 2024
Cited alongside, same era.
System-1. x: Learning to balance fast and slow planning with language models
Swarnadeep Saha, Archiki Prasad, Justin Chih-Yao Chen, Peter Hase, Elias Stengel-Eskin, and Mohit Bansal · 2024
Cited alongside, same era.
Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
Cited alongside, same era.
The good, the bad, and the greedy: Evaluation of llms should not ignore non-determinism
Yifan Song, Guoyin Wang, Sujian Li, and Bill Yuchen Lin · 2024
Cited alongside, same era.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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C3ot: Generating shorter chain-of-thought without compromising effectiveness
Yu Kang, Xianghui Sun, Liangyu Chen, and Wei Zou · 2025
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Bespoke-stratos: The unreasonable effectiveness of reasoning distillation
Bespoke Labs · 2025
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Llms can easily learn to reason from demonstrations structure, not content, is what matters!
Dacheng Li, Shiyi Cao, Tyler Griggs, Shu Liu, Xiangxi Mo, Eric Tang, Sumanth Hegde, Kourosh Hakhamaneshi, Shishir G Patil, Matei Zaharia, et al · 2025
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O1-pruner: Length-harmonizing fine-tuning for o1-like reasoning pruning
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Dualformer: Controllable fast and slow thinking by learning with randomized reasoning traces
DiJia Su, Sainbayar Sukhbaatar, Michael Rabbat, Yuandong Tian, and Qinqing Zheng · 2024
Cited alongside, same era.
Toward self-improvement of llms via imagination, searching, and criticizing
Ye Tian, Baolin Peng, Linfeng Song, Lifeng Jin, Dian Yu, Lei Han, Haitao Mi, and Dong Yu · 2024
Cited alongside, same era.
An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al · 2024
Cited alongside, same era.
https://www.anthropic.com/news/claude-3-7-sonnet
Claude 3.7 Sonnet and Claude Code — anthropic.com · 2025
Cited alongside, same era.
https://cloud.google.com/vertex-ai/generative-ai/docs/thinking
Thinking | Generative AI on Vertex AI | Google Cloud — cloud.google.com · 2025
Cited alongside, same era.
Yingqian Cui, Pengfei He, Jingying Zeng, Hui Liu, Xianfeng Tang, Zhenwei Dai, Yan Han, Chen Luo, Jing Huang, Zhen Li, et al · 2025
Cited alongside, same era.
Missing premise exacerbates overthinking: Are reasoning models losing critical thinking skill?
Chenrui Fan, Ming Li, Lichao Sun, and Tianyi Zhou · 2025
Cited alongside, same era.
Cognitive behaviors that enable self-improving reasoners, or, four habits of highly effective stars
Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, Nathan Lile, and Noah D Goodman · 2025
Cited alongside, same era.
Haotian Luo, Li Shen, Haiying He, Yibo Wang, Shiwei Liu, Wei Li, Naiqiang Tan, Xiaochun Cao, and Dacheng Tao · 2025
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Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto · 2025
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Optimizing test-time compute via meta reinforcement fine-tuning
Yuxiao Qu, Matthew YR Yang, Amrith Setlur, Lewis Tunstall, Edward Emanuel Beeching, Ruslan Salakhutdinov, and Aviral Kumar · 2025
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Dast: Difficulty-adaptive slow-thinking for large reasoning models
Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi, Wenjing Zhang, Jiangze Yan, Ning Wang, Kai Wang, and Shiguo Lian · 2025
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Qwq-32b: Embracing the power of reinforcement learning, March 2025
Qwen Team · 2025
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Thoughts are all over the place: On the underthinking of o1-like llms
Yue Wang, Qiuzhi Liu, Jiahao Xu, Tian Liang, Xingyu Chen, Zhiwei He, Linfeng Song, Dian Yu, Juntao Li, Zhuosheng Zhang, et al · 2025
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Tokenskip: Controllable chain-of-thought compression in llms
Heming Xia, Yongqi Li, Chak Tou Leong, Wenjie Wang, and Wenjie Li · 2025
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Limo: Less is more for reasoning
Yixin Ye, Zhen Huang, Yang Xiao, Ethan Chern, Shijie Xia, and Pengfei Liu · 2025
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