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Large Language Models (LLMs) trained via Reinforcement Learning (RL) have recently achieved impressive results on reasoning benchmarks.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Earlier work this paper cites.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
Earlier work this paper cites.
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 · 2022
Earlier work this paper cites.
Critique-out-loud reward models
Zachary Ankner, Mansheej Paul Brandon Cui, Jonathan D. Chang, and Prithviraj Ammanabrolu. 2023 · 2023
Earlier work this paper cites.
Have llms advanced enough? a challenging problem solving benchmark for large language models
Daman Arora and Himanshu Gaurav Singh. 2023 · 2023
Earlier work this paper cites.
CodePlan: Repository-level Coding using LLMs and Planning
Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade, Vageesh D C, Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, B. Ashok, and Shashank Shet. 2023 · 2023
Earlier work this paper cites.
Sparse autoencoders find highly interpretable features in language models
Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey. 2023 · 2023
Earlier work this paper cites.
Interpretable contrastive monte carlo tree search reasoning
Zitian Gao, Boye Niu, Xuzheng He, Haotian Xu, Hongzhang Liu, Aiwei Liu, Xuming Hu, and Lijie Wen. 2023 · 2023
Earlier work this paper cites.
Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models
Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim. 2023 · 2023
Earlier work this paper cites.
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 · 2023
Earlier work this paper cites.
Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs
Kewei Cheng, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Binxuan Huang, Ruirui Li, Shiyang Li, Zheng Li, Yifan Gao, Xian Li, Bing Yin, and Yizhou Sun. 2024 · 2024
Earlier work this paper cites.
Transcoders find interpretable llm feature circuits
Jacob Dunefsky, Philippe Chlenski, and Neel Nanda. 2024 · 2024
Earlier work this paper cites.
Llms can’t plan, but can help planning in llm-modulo frameworks
Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri, Lucas Saldyt, and Anil Murthy. 2024 · 2024
Cited alongside, same era.
Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2
Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca Dragan, Rohin Shah, and Neel Nanda. 2024 · 2024
Cited alongside, same era.
Openai o1 system card
OpenAI. 2024 · 2024
Cited alongside, same era.
Qwq: Reflect deeply on the boundaries of the unknown
Qwen-Team. 2024 · 2024
Cited alongside, same era.
Group robust preference optimization in reward-free rlhf
Shyam Sundhar Ramesh, Yifan Hu, Iason Chaimalas, Viraj Mehta, Pier Giuseppe Sessa, Haitham Bou Ammar, and Ilija Bogunovic. 2024 · 2024
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
DeepSeek-AI. 2025 · 2025
Closest in time.
Advancing language model reasoning through reinforcement learning and inference scaling
Zhenyu Hou, Xin Lv, Rui Lu, Jiajie Zhang, Yujiang Li, Zijun Yao, Juanzi Li, Jie Tang, and Yuxiao Dong. 2025 · 2025
Closest in time.
Tulu 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, and 4 others. 2025 · 2025
Closest in time.
On the biology of a large language model
Jack Lindsey, Wes Gurnee, Emmanuel Ameisen, Brian Chen, Adam Pearce, Nicholas L. Turner, Craig Citro, David Abrahams, Shan Carter, Basil Hosmer, Jonathan Marcus, Michael Sklar, Adly Templeton, Trenton Bricken, Callum McDougall, Hoagy Cunningham, Thomas Henighan, Adam Jermyn, Andy Jones, and 8 others. 2025 · 2025
Closest in time.
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Cited alongside, same era.
Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
Ye Tian, Baolin Peng, Linfeng Song, Lifeng Jin, Dian Yu, Haitao Mi, and Dong Yu. 2024 · 2024
Cited alongside, same era.
Llms still can’t plan; can lrms? a preliminary evaluation of openai’s o1 on planbench
Karthik Valmeekam, Kaya Stechly, and Subbarao Kambhampati. 2024 · 2024
Cited alongside, same era.
Mixture-of-Agents Enhances Large Language Model Capabilities
Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, and James Zou. 2024 · 2024
Cited alongside, same era.
Circuit tracing: Revealing computational graphs in language models
Emmanuel Ameisen, Jack Lindsey, Adam Pearce, Wes Gurnee, Nicholas L. Turner, Brian Chen, Craig Citro, David Abrahams, Shan Carter, Basil Hosmer, Jonathan Marcus, Michael Sklar, Adly Templeton, Trenton Bricken, Callum McDougall, Hoagy Cunningham, Thomas Henighan, Adam Jermyn, Andy Jones, and 8 others. 2025 · 2025
Cited alongside, same era.
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks
Maksym Andriushchenko, Francesco Croce, and Nicolas Flammarion. 2025 · 2025
Cited alongside, same era.
Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
Bradley Brown, Jordan Juravsky, Ryan Saul Ehrlich, Ronald Clark, Quoc V Le, Christopher Re, and Azalia Mirhoseini. 2025 · 2025
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, Rui Wang, Zhaopeng Tu, Haitao Mi, and Dong Yu. 2025 · 2025
Cited alongside, same era.
Deepscaler: Surpassing o1-preview with a 1.5b model by scaling rl
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 · 2025
Closest in time.
Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. 2025 · 2025
Closest in time.
Openai o3 and o4-mini system card
OpenAI. 2025 · 2025
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Optimizing language models for inference time objectives using reinforcement learning
Yunhao Tang, Kunhao Zheng, Gabriel Synnaeve, and Rémi Munos. 2025 · 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, Rui Wang, Zhaopeng Tu, Haitao Mi, and Dong Yu. 2025 · 2025
Closest in time.
Demystifying long chain-of-thought reasoning in llms
Edward Yeo, Yuxuan Tong, Morry Niu, Graham Neubig, and Xiang Yue. 2025 · 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 · 2025
Closest in time.
Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design
Zhi Zheng, Zhuoliang Xie, Zhenkun Wang, and Bryan Hooi. 2025 · 2025
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