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Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing Large Language Models (LLMs), exemplified by the success of OpenAI's o-series.
Socialiqa: Commonsense reasoning about social interactions, 2019
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, and Yejin Choi · 1904
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Hellaswag: Can a machine really finish your sentence?, 2019
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 1905
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Measuring massive multitask language understanding, 2021a
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2009
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A simple and effective approach to the story cloze test, 2018
Siddarth Srinivasan, Richa Arora, and Mark Riedl · 2018
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Coqa: A conversational question answering challenge, 2019
Siva Reddy, Danqi Chen, and Christopher D. Manning · 2019
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Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
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Self-critiquing models for assisting human evaluators, 2022
William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike · 2022
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Gpqa: A graduate-level google-proof q&a benchmark, 2023
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman · 2023
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Salmon: Self-alignment with instructable reward models
Zhiqing Sun, Yikang Shen, Hongxin Zhang, Qinhong Zhou, Zhenfang Chen, David Daniel Cox, Yiming Yang, and Chuang Gan · 2023
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Collie: Systematic construction of constrained text generation tasks, 2023
Shunyu Yao, Howard Chen, Austin W. Hanjie, Runzhe Yang, and Karthik Narasimhan · 2023
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Instruction-following evaluation for large language models, 2023
Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou · 2023
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Tombench: Benchmarking theory of mind in large language models, 2024
Zhuang Chen, Jincenzi Wu, Jinfeng Zhou, Bosi Wen, Guanqun Bi, Gongyao Jiang, Yaru Cao, Mengting Hu, Yunghwei Lai, Zexuan Xiong, and Minlie Huang · 2024
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Deliberative alignment: Reasoning enables safer language models
Melody Y Guan, Manas Joglekar, Eric Wallace, Saachi Jain, Boaz Barak, Alec Helyar, Rachel Dias, Andrea Vallone, Hongyu Ren, Jason Wei, et al · 2024
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Livecodebench: Holistic and contamination free evaluation of large language models for code, 2024
Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica · 2024
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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, et al · 2024
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Aime 2024
Math-AI · 2024
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Llm critics help catch llm bugs, 2024
Nat McAleese, Rai Michael Pokorny, Juan Felipe Ceron Uribe, Evgenia Nitishinskaya, Maja Trebacz, and Jan Leike · 2024
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Rule based rewards for language model safety
Tong Mu, Alec Helyar, Johannes Heidecke, Joshua Achiam, Andrea Vallone, Ian Kivlichan, Molly Lin, Alex Beutel, John Schulman, and Lilian Weng · 2024
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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Open r1: A fully open reproduction of deepseek-r1, January 2025
Hugging Face · 2025
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How many instructions can llms follow at once?, 2025
Daniel Jaroslawicz, Brendan Whiting, Parth Shah, and Karime Maamari · 2025
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Search-r1: Training llms to reason and leverage search engines with reinforcement learning
Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han · 2025
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Omni-think: Scaling cross-domain generalization in llms via multi-task rl with hybrid rewards
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Learning to reason with llms, 2024
OpenAI · 2024
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Eq-bench: An emotional intelligence benchmark for large language models, 2024
Samuel J. Paech · 2024
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Healthbench: Evaluating large language models towards improved human health
Rahul K Arora, Jason Wei, Rebecca Soskin Hicks, Preston Bowman, Joaquin Quiñonero-Candela, Foivos Tsimpourlas, Michael Sharman, Meghan Shah, Andrea Vallone, Alex Beutel, et al · 2025
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Gemini models, 2025
Google DeepMind · 2025
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Deepseek-v3 technical report, 2025
DeepSeek-AI, Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al · 2025
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Areal: A large-scale asynchronous reinforcement learning system for language reasoning, 2025
Wei Fu, Jiaxuan Gao, Xujie Shen, Chen Zhu, Zhiyu Mei, Chuyi He, Shusheng Xu, Guo Wei, Jun Mei, Jiashu Wang, Tongkai Yang, Binhang Yuan, and Yi Wu · 2025
Cited alongside, same era.
Rubrics as rewards: Reinforcement learning beyond verifiable domains
Anisha Gunjal, Anthony Wang, Elaine Lau, Vaskar Nath, Bing Liu, and Sean Hendryx · 2025
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Derek Li, Jiaming Zhou, Amirreza Kazemi, Qianyi Sun, Abbas Ghaddar, Mohammad Ali Alomrani, Liheng Ma, Yu Luo, Dong Li, Feng Wen, et al · 2025
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Aime 2025
Math-AI · 2025
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Introducing openai o3 and o4-mini, 2025
OpenAI · 2025
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Checklists are better than reward models for aligning language models
Vijay Viswanathan, Yanchao Sun, Shuang Ma, Xiang Kong, Meng Cao, Graham Neubig, and Tongshuang Wu · 2025
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Safety reasoning with guidelines
Haoyu Wang, Zeyu Qin, Li Shen, Xueqian Wang, Dacheng Tao, and Minhao Cheng · 2025
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Writingbench: A comprehensive benchmark for generative writing
Yuning Wu, Jiahao Mei, Ming Yan, Chenliang Li, Shaopeng Lai, Yuran Ren, Zijia Wang, Ji Zhang, Mengyue Wu, Qin Jin, et al · 2025
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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
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An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al · 2025
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