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Reinforcement Learning (RL)-based post-training has significantly advanced the complex reasoning capabilities of language models, fostering sophisticated self-reflection processes.
Peft: State-of-the-art parameter-efficient fine-tuning methods
Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, Sayak Paul, and Benjamin Bossan · 2022
Earlier work this paper cites.
Self-critiquing models for assisting human evaluators
William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike · 2022
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Selfcheck: Using llms to zero-shot check their own step-by-step reasoning
Ning Miao, Yee Whye Teh, and Tom Rainforth · 2023
Earlier work this paper cites.
Large language models are better reasoners with self-verification
Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, and Jun Zhao · 2023
Earlier work this paper cites.
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
Earlier work this paper cites.
Token-budget-aware llm reasoning
Tingxu Han, Zhenting Wang, Chunrong Fang, Shiyu Zhao, Shiqing Ma, and Zhenyu Chen · 2024
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The impact of reasoning step length on large language models
Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, and Mengnan Du · 2024
Earlier work this paper cites.
Vineppo: Unlocking rl potential for llm reasoning through refined credit assignment
Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron Courville, and Nicolas Le Roux · 2024
Earlier work this paper cites.
Concise thoughts: Impact of output length on llm reasoning and cost
Sania Nayab, Giulio Rossolini, Marco Simoni, Andrea Saracino, Giorgio Buttazzo, Nicolamaria Manes, and Fabrizio Giacomelli · 2024
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Learning to reason with LLMs
OpenAI · 2024
Earlier work this paper cites.
The benefits of a concise chain of thought on problem-solving in large language models
Matthew Renze and Erhan Guven · 2024
Cited alongside, same era.
Self-reflection in llm agents: Effects on problem-solving performance
Matthew Renze and Erhan Guven · 2024
Cited alongside, same era.
L1: Controlling how long a reasoning model thinks with reinforcement learning
Pranjal Aggarwal and Sean Welleck · 2025
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
Cited alongside, same era.
C3ot: Generating shorter chain-of-thought without compromising effectiveness
Yu Kang, Xianghui Sun, Liangyu Chen, and Wei Zou · 2025
Maohao Shen, Guangtao Zeng, Zhenting Qi, Zhang-Wei Hong, Zhenfang Chen, Wei Lu, Gregory Wornell, Subhro Das, David Cox, and Chuang Gan · 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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Stop overthinking: A survey on efficient reasoning for large language models
Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen Zhong, Hanjie Chen, et al · 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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Cited alongside, same era.
Llm post-training: A deep dive into reasoning large language models
Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip HS Torr, Fahad Shahbaz Khan, and Salman Khan · 2025
Cited alongside, same era.
Adaptivestep: Automatically dividing reasoning step through model confidence
Yuliang Liu, Junjie Lu, Zhaoling Chen, Chaofeng Qu, Jason Klein Liu, Chonghan Liu, Zefan Cai, Yunhui Xia, Li Zhao, Jiang Bian, et al · 2025
Cited alongside, same era.
Deepscaler: Surpassing o1-preview with a 1.5b model by scaling rl, 2025
Michael Luo, Sijun Tan, Justin Wong, Xiaoxiang Shi, William Y. Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica · 2025
Cited alongside, same era.
Yan Ma, Steffi Chern, Xuyang Shen, Yiran Zhong, and Pengfei Liu · 2025
Cited alongside, same era.
Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto · 2025
Cited alongside, same era.
Logic-rl: Unleashing llm reasoning with rule-based reinforcement learning
Tian Xie, Zitian Gao, Qingnan Ren, Haoming Luo, Yuqian Hong, Bryan Dai, Joey Zhou, Kai Qiu, Zhirong Wu, and Chong Luo · 2025
Closest in time.
A minimalist approach to llm reasoning: from rejection sampling to reinforce
Wei Xiong, Jiarui Yao, Yuhui Xu, Bo Pang, Lei Wang, Doyen Sahoo, Junnan Li, Nan Jiang, Tong Zhang, Caiming Xiong, et al · 2025
Closest in time.
Chain of draft: Thinking faster by writing less
Silei Xu, Wenhao Xie, Lingxiao Zhao, and Pengcheng He · 2025
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Demystifying long chain-of-thought reasoning in llms
Edward Yeo, Yuxuan Tong, Morry Niu, Graham Neubig, and Xiang Yue · 2025
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Dapo: An open-source llm reinforcement learning system at scale
Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, et al · 2025
Closest in time.
Srpo: A cross-domain implementation of large-scale reinforcement learning on llm
Xiaojiang Zhang, Jinghui Wang, Zifei Cheng, Wenhao Zhuang, Zheng Lin, Minglei Zhang, Shaojie Wang, Yinghan Cui, Chao Wang, Junyi Peng, et al · 2025
Closest in time.