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Recent generations of language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers.
On representations of problems of reasoning about actions
Saul Amarel · 1981
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Crossing the bridge at night
Günter Rote · 2002
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The algebraic mind: Integrating connectionism and cognitive science
Gary F Marcus · 2003
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On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou · 2022
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Lambada: Backward chaining for automated reasoning in natural language
Mehran Kazemi, Najoung Kim, Deepti Bhatia, Xin Xu, and Deepak Ramachandran · 2022
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Teaching algorithmic reasoning via in-context learning
Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron Courville, Behnam Neyshabur, and Hanie Sedghi · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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STar: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman · 2022
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Large language models still can’t plan (A benchmark for llms on planning and reasoning about change)
Karthik Valmeekam, Alberto Olmo Hernandez, Sarath Sreedharan, and Subbarao Kambhampati · 2022
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
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Faith and fate: Limits of transformers on compositionality
Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jiang, Bill Yuchen Lin, Sean Welleck, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D. Hwang, Soumya Sanyal, Xiang Ren, Allyson Ettinger, Zaïd Harchaoui, and Yejin Choi · 2023
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Embers of autoregression: Understanding large language models through the problem they are trained to solve, 2023
R. Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, and Thomas L. Griffiths · 2023
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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
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Making language models better reasoners with step-aware verifier
Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, and Weizhu Chen · 2023
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Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe · 2023
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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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Introducing openai o1
OpenAI · 2024
Cited alongside, same era.
Phi-3 technical report: A highly capable language model locally on your phone
Marah I Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat S. Behl, and et. al · 2024
Cited alongside, same era.
Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, and Jenia Jitsev · 2024
Cited alongside, same era.
Think before you speak: Training language models with pause tokens
Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, and Vaishnavh Nagarajan · 2024
Cited alongside, same era.
Thinking tokens for language modeling
David Herel and Tomas Mikolov · 2024
GSM-symbolic: Understanding the limitations of mathematical reasoning in large language models
Seyed Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, and Mehrdad Farajtabar · 2025
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Arc-agi-2: A new challenge for frontier ai reasoning systems
Francois Chollet, Mike Knoop, Gregory Kamradt, Bryan Landers, and Henry Pinkard · 2025
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Sample, scrutinize and scale: Effective inference-time search by scaling verification
Eric Zhao, Pranjal Awasthi, and Sreenivas Gollapudi · 2025
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Reasoning models don’t always say what they think
Yanda Chen, Joe Benton, Ansh Radhakrishnan, Jonathan Uesato, Carson Denison, John Schulman, Arushi Somani, Peter Hase, Misha Wagner, Fabien Roger, et al · 2025
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Llms can easily learn to reason from demonstrations structure, not content, is what matters!
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Cited alongside, same era.
Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024
Zhihong Shao, Peiyi Wang, Runxin Xu Qihao Zhu, Junxiao Song, Mingchuan Zhang, Y.K. Li, Y. Wu, and Daya Guo · 2024
Cited alongside, same era.
Vineppo: Unlocking rl potential for llm reasoning through refined credit assignment, 2024
Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron Courville, and Nicolas Le Roux · 2024
Cited alongside, same era.
Tülu 3: Pushing frontiers in open language model post-training
Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James V. Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, Yuling Gu, Saumya Malik, Victoria Graf, Jena D. Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord, Chris Wilhelm, Luca Soldaini, Noah A. Smith, Yizhong Wang, Pradeep Dasigi, and Hannaneh Hajishirzi · 2024
Cited alongside, same era.
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
Cited alongside, same era.
Limits of deep learning: Sequence modeling through the lens of complexity theory
Nikola Zubić, Federico Soldá, Aurelio Sulser, and Davide Scaramuzza · 2024
Cited alongside, same era.
Puzzles: A benchmark for neural algorithmic reasoning, 2024
Benjamin Estermann, Luca A. Lanzendörfer, Yannick Niedermayr, and Roger Wattenhofer · 2024
Cited alongside, same era.
Lmact: A benchmark for in-context imitation learning with long multimodal demonstrations
Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li, Volodymyr Mnih, and Tim Genewein · 2024
Cited alongside, same era.
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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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, Hanjie Chen, Xia Hu, et al · 2025
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Deepseek-r1 thoughtology: Let’s< think> about llm reasoning
Sara Vera Marjanović, Arkil Patel, Vaibhav Adlakha, Milad Aghajohari, Parishad BehnamGhader, Mehar Bhatia, Aditi Khandelwal, Austin Kraft, Benno Krojer, Xing Han Lù, et al · 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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Marthe Ballon, Andres Algaba, and Vincent Ginis · 2025
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Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model?
Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Shiji Song, and Gao Huang · 2025
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Reasoning models can be effective without thinking
Wenjie Ma, Jingxuan He, Charlie Snell, Tyler Griggs, Sewon Min, and Matei Zaharia · 2025
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American invitational mathematics examination (aime)
Mathematical Association of America · 2025
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Amc historical results - aime i (february 1, 2024)
Art of Problem Solving · 2025
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Amc historical results – aime i (february 6, 2025)
Art of Problem Solving · 2025
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Improving large language model planning with action sequence similarity
Xinran Zhao, Hanie Sedghi, Bernd Bohnet, Dale Schuurmans, and Azade Nova · 2025
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When thinking fails: The pitfalls of reasoning for instruction-following in llms
Xiaomin Li, Zhou Yu, Zhiwei Zhang, Xupeng Chen, Ziji Zhang, Yingying Zhuang, Narayanan Sadagopan, and Anurag Beniwal · 2025
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