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Reasoning has emerged as the next major frontier for language models (LMs), with rapid advances from both academic and industrial labs.
What matters in on-policy reinforcement learning? a large-scale empirical study, 2020
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphael Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, and Olivier Bachem · 2006
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On over-fitting in model selection and subsequent selection bias in performance evaluation
Gavin C Cawley and Nicola LC Talbot · 2010
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Baseline methods for active learning
Gavin C Cawley · 2011
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Re-evaluating evaluation
David Balduzzi, Karl Tuyls, Julien Perolat, and Thore Graepel · 2018
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How many random seeds? statistical power analysis in deep reinforcement learning experiments
Cédric Colas, Olivier Sigaud, and Pierre-Yves Oudeyer · 2018
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The hitchhiker’s guide to testing statistical significance in natural language processing
Rotem Dror, Gili Baumer, Segev Shlomov, and Roi Reichart · 2018
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
Marlos C Machado, Marc G Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling · 2018
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Measuring the reliability of reinforcement learning algorithms
Stephanie CY Chan, Samuel Fishman, John Canny, Anoop Korattikara, and Sergio Guadarrama · 2019
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Troubling trends in machine learning scholarship: Some ml papers suffer from flaws that could mislead the public and stymie future research
Zachary C Lipton and Jacob Steinhardt · 2019
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With little power comes great responsibility
Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, and Dan Jurafsky · 2020
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Evaluating the performance of reinforcement learning algorithms
Scott Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, and Philip Thomas · 2020
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A metric learning reality check
Kevin Musgrave, Serge Belongie, and Ser-Nam Lim · 2020
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Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip HS Torr, and Puneet K Dokania · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2021
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Mostafa Dehghani, Yi Tay, Alexey A Gritsenko, Zhe Zhao, Neil Houlsby, Fernando Diaz, Donald Metzler, and Oriol Vinyals · 2021
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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Are we learning yet? A meta review of evaluation failures across machine learning
Thomas Liao, Rohan Taori, Inioluwa Deborah Raji, and Ludwig Schmidt · 2021
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Scientific credibility of machine translation research: A meta-evaluation of 769 papers
Benjamin Marie, Atsushi Fujita, and Raphael Rubino · 2021
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Towards a standardised performance evaluation protocol for cooperative marl
Rihab Gorsane, Omayma Mahjoub, Ruan John de Kock, Roland Dubb, Siddarth Singh, and Arnu Pretorius · 2022
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Evaluation gaps in machine learning practice
Ben Hutchinson, Negar Rostamzadeh, Christina Greer, Katherine Heller, and Vinodkumar Prabhakaran · 2022
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Solving quantitative reasoning problems with language models
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, and Vedant Misra · 2022
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Solving math word problems with process-and outcome-based feedback
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins · 2022
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LightEval: A lightweight framework for LLM evaluation, 2023
Clémentine Fourrier, Nathan Habib, Hynek Kydlíček, Thomas Wolf, and Lewis Tunstall · 2023
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Time travel in llms: Tracing data contamination in large language models
Shahriar Golchin and Mihai Surdeanu · 2023
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Large language models struggle to learn long-tail knowledge
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel · 2023
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Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica · 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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Eureka: Human-level reward design via coding large language models
Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2023
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Rdumb: A simple approach that questions our progress in continual test-time adaptation
Ori Press, Steffen Schneider, Matthias Kümmerer, and Matthias Bethge · 2023
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To the cutoff… and beyond? a longitudinal perspective on llm data contamination
Manley Roberts, Himanshu Thakur, Christine Herlihy, Colin White, and Samuel Dooley · 2023
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Optimizing large language model hyperparameters for code generation
Chetan Arora, Ahnaf Ibn Sayeed, Sherlock Licorish, Fanyu Wang, and Christoph Treude · 2024
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Lessons from the trenches on reproducible evaluation of language models, 2024
Stella Biderman, Hailey Schoelkopf, Lintang Sutawika, Leo Gao, Jonathan Tow, Baber Abbasi, Alham Fikri Aji, Pawan Sasanka Ammanamanchi, Sidney Black, Jordan Clive, Anthony DiPofi, Julen Etxaniz, Benjamin Fattori, Jessica Zosa Forde, Charles Foster, Jeffrey Hsu, Mimansa Jaiswal, Wilson Y. Lee, Haonan Li, Charles Lovering, Niklas Muennighoff, Ellie Pavlick, Jason Phang, Aviya Skowron, Samson Tan, Xiangru Tang, Kevin A. Wang, Genta Indra Winata, François Yvon, and Andy Zou · 2024
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Training on the test task confounds evaluation and emergence
Ricardo Dominguez-Olmedo, Florian E Dorner, and Moritz Hardt · 2024
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Onebench to test them all: Sample-level benchmarking over open-ended capabilities
Adhiraj Ghosh, Sebastian Dziadzio, Ameya Prabhu, Vishaal Udandarao, Samuel Albanie, and Matthias Bethge · 2024
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Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, and Maosong Sun · 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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Rlsf: Reinforcement learning via symbolic feedback
Piyush Jha, Prithwish Jana, Pranavkrishna Suresh, Arnav Arora, and Vijay Ganesh · 2024
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Position: Benchmarking is limited in reinforcement learning research
Scott M Jordan, Adam White, Bruno Castro Da Silva, Martha White, and Philip S Thomas · 2024
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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
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Datacomp-lm: In search of the next generation of training sets for language models
Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Guha, Sedrick Scott Keh, Kushal Arora, et al · 2024
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Are your llms capable of stable reasoning?
Junnan Liu, Hongwei Liu, Linchen Xiao, Ziyi Wang, Kuikun Liu, Songyang Gao, Wenwei Zhang, Songyang Zhang, and Kai Chen · 2024
Cited alongside, same era.
Quantifying variance in evaluation benchmarks, 2024
Lovish Madaan, Aaditya K Singh, Rylan Schaeffer, Andrew Poulton, Sanmi Koyejo, Pontus Stenetorp, Sharan Narang, and Dieuwke Hupkes · 2024
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Imitate, explore, and self-improve: A reproduction report on slow-thinking reasoning systems, 2024
Yingqian Min, Zhipeng Chen, Jinhao Jiang, Jie Chen, Jia Deng, Yiwen Hu, Yiru Tang, Jiapeng Wang, Xiaoxue Cheng, Huatong Song, Wayne Xin Zhao, Zheng Liu, Zhongyuan Wang, and Ji-Rong Wen · 2024
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Math-500 dataset
HuggingFaceH4 · 2025
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II-Thought : A Large-Scale, High-Quality Reasoning Dataset, 2025
Intelligent Internet · 2025
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Amc-23 dataset, 2025
Knovel Engineering · 2025
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Aime 2025 dataset, 2025
Yen-Ting Lin · 2025
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Code-r1: Reproducing r1 for code with reliable rewards
Jiawei Liu and Lingming Zhang · 2025
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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, Tianjun Zhang, Li Erran Li, Raluca Ada Popa, and Ion Stoica · 2025
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Exploring the limit of outcome reward for learning mathematical reasoning, 2025
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Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, and Mehrdad Farajtabar · 2024
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Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, and Jenia Jitsev · 2024
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The neglected tails in vision-language models
Shubham Parashar, Zhiqiu Lin, Tian Liu, Xiangjue Dong, Yanan Li, Deva Ramanan, James Caverlee, and Shu Kong · 2024
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Empirical design in reinforcement learning
Andrew Patterson, Samuel Neumann, Martha White, and Adam White · 2024
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The effect of sampling temperature on problem solving in large language models
Matthew Renze · 2024
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BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices
Anka Reuel, Amelia Hardy, Chandler Smith, Max Lamparth, Malcolm Hardy, and Mykel J Kochenderfer · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y. K. Li, Y. Wu, and Daya Guo · 2024
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Critique of impure reason: Unveiling the reasoning behaviour of medical large language models
Shamus Sim and Tyrone Chen · 2024
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Chengqi Lyu, Songyang Gao, Yuzhe Gu, Wenwei Zhang, Jianfei Gao, Kuikun Liu, Ziyi Wang, Shuaibin Li, Qian Zhao, Haian Huang, Weihan Cao, Jiangning Liu, Hongwei Liu, Junnan Liu, Songyang Zhang, Dahua Lin, and Kai Chen · 2025
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Yan Ma, Steffi Chern, Xuyang Shen, Yiran Zhong, and Pengfei Liu · 2025
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Fanqing Meng, Lingxiao Du, Zongkai Liu, Zhixiang Zhou, Quanfeng Lu, Daocheng Fu, Tiancheng Han, Botian Shi, Wenhai Wang, Junjun He, Kaipeng Zhang, Ping Luo, Yu Qiao, Qiaosheng Zhang, and Wenqi Shao · 2025
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The llama 4 herd: The beginning of a new era of natively multimodal ai innovation, 2025
Meta-AI · 2025
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s1: Simple test-time scaling, 2025
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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Open Thoughts
Open Thoughts · 2025
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Lmm-r1: Empowering 3b lmms with strong reasoning abilities through two-stage rule-based rl
Yingzhe Peng, Gongrui Zhang, Miaosen Zhang, Zhiyuan You, Jie Liu, Qipeng Zhu, Kai Yang, Xingzhong Xu, Xin Geng, and Xu Yang · 2025
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Proof or bluff? evaluating llms on 2025 usa math olympiad
Ivo Petrov, Jasper Dekoninck, Lyuben Baltadzhiev, Maria Drencheva, Kristian Minchev, Mislav Balunović, Nikola Jovanović, and Martin Vechev · 2025
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Intellect-2: A reasoning model trained through globally decentralized reinforcement learning
Prime Intellect · 2025
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Reproducibility — pytorch documentation
PyTorch Contributors · 2025
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Qwen2.5 technical report, 2025
Qwen, :, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tianyi Tang, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu · 2025
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Evalchemy: Automatic evals for LLMs, June 2025
Negin Raoof, Etash Kumar Guha, Ryan Marten, Jean Mercat, Eric Frankel, Sedrick Keh, Hritik Bansal, Georgios Smyrnis, Marianna Nezhurina, Trung Vu, Zayne Rea Sprague, Mike A Merrill, Liangyu Chen, Caroline Choi, Zaid Khan, Sachin Grover, Benjamin Feuer, Ashima Suvarna, Shiye Su, Wanjia Zhao, Kartik Sharma, Charlie Cheng-Jie Ji, Kushal Arora, Jeffrey Li, Aaron Gokaslan, Sarah M Pratt, Niklas Muennighoff, Jon Saad-Falcon, John Yang, Asad Aali, Shreyas Pimpalgaonkar, Alon Albalak, Achal Dave, Hadi Pouransari, Greg Durrett, Sewoong Oh, Tatsunori Hashimoto, Vaishaal Shankar, Yejin Choi, Mohit Bansal, Chinmay Hegde, Reinhard Heckel, Jenia Jitsev, Maheswaran Sathiamoorthy, Alex Dimakis, and Ludwig Schmidt · 2025
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Zerobench: An impossible visual benchmark for contemporary large multimodal models, 2025
Jonathan Roberts, Mohammad Reza Taesiri, Ansh Sharma, Akash Gupta, Samuel Roberts, Ioana Croitoru, Simion-Vlad Bogolin, Jialu Tang, Florian Langer, Vyas Raina, Vatsal Raina, Hanyi Xiong, Vishaal Udandarao, Jingyi Lu, Shiyang Chen, Sam Purkis, Tianshuo Yan, Wenye Lin, Gyungin Shin, Qiaochu Yang, Anh Totti Nguyen, David I. Atkinson, Aaditya Baranwal, Alexandru Coca, Mikah Dang, Sebastian Dziadzio, Jakob D. Kunz, Kaiqu Liang, Alexander Lo, Brian Pulfer, Steven Walton, Charig Yang, Kai Han, and Samuel Albanie · 2025
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Tapered off-policy reinforce: Stable and efficient reinforcement learning for llms
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Grok 3 beta — the age of reasoning agents
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Limo: Less is more for reasoning
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Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, Haibin Lin, Zhiqi Lin, Bole Ma, Guangming Sheng, Yuxuan Tong, Chi Zhang, Mofan Zhang, Wang Zhang, Hang Zhu, Jinhua Zhu, Jiaze Chen, Jiangjie Chen, Chengyi Wang, Hongli Yu, Yuxuan Song, Xiangpeng Wei, Hao Zhou, Jingjing Liu, Wei-Ying Ma, Ya-Qin Zhang, Lin Yan, Mu Qiao, Yonghui Wu, and Mingxuan Wang · 2025
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Naturalreasoning: Reasoning in the wild with 2.8m challenging questions, 2025
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Med-rlvr: Emerging medical reasoning from a 3b base model via reinforcement learning
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Zyphra · 2025
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