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We introduce Reasoning Gym (RG), a library of reasoning environments for reinforcement learning with verifiable rewards.
Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc' Aurelio Ranzato · 2017
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Dynamic few-shot visual learning without forgetting, 2018
Spyros Gidaris and Nikos Komodakis · 2018
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Progress and compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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Joaquin Vanschoren · 2018
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On the measure of intelligence
François Chollet · 2019
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Unsupervised curricula for visual meta-reinforcement learning
Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, and Chelsea Finn · 2019
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Human-level performance in 3d multiplayer games with population-based reinforcement learning
Max Jaderberg, Wojciech M Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C Rabinowitz, Ari S Morcos, Avraham Ruderman, et al · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Dark experience for general continual learning: a strong, simple baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and SIMONE CALDERARA · 2020
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Leveraging procedural generation to benchmark reinforcement learning, 2020
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman · 2020
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Probabilistic active meta-learning
Jean Kaddour, Steindór Sæmundsson, et al · 2020
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The nethack learning environment
Heinrich Küttler, Nantas Nardelli, Alexander Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, and Tim Rocktäschel · 2020
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Curriculum learning for reinforcement learning domains: A framework and survey
Sanjay Narvekar, Bo Peng, Marco Leonetti, Jivko Sinapov, and Matthew E. Taylor · 2020
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Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip Torr, and Puneet Dokania · 2020
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Increasing generality in machine learning through procedural content generation
Sebastian Risi and Julian Togelius · 2020
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Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
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Evaluating large language models trained on code, 2021
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
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Training verifiers to solve math word problems, 2021
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 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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Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2021
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Dynabench: Rethinking benchmarking in nlp
Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, et al · 2021
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Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
Jane X Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charlie Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, Francis Song, et al · 2021
Cited alongside, same era.
Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Scott Miller, Eli Tran-Johnson, Zac Hatfield-Dodds, Danny Hernandez, Jackson Kernion, Liane Lovitt, Karolina Misztal, Alecia Paulus, Arka Pal, Maribeth Rauh, Jihan Sang, Sheer El-Showk, Jan Brauner, Matthew Bussing, David Choi, David Schnurr, Ethan Perez, Stuart Armstrong, Sandipan Kundu, Ephraim Christopher, Catherine Olsson, John Schulman, Samuel R. Bowman, Ethan Caballero, Christopher Olah, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Jared Kaplan, and Dario Amodei · 2022
Cited alongside, same era.
When do flat minima optimizers work?
Jean Kaddour, Linqing Liu, Ricardo Silva, and Matt J Kusner · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback, 2022
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 2022
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, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu · 2024
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Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, et al · 2024
Later among the works it cites.
Opencodereasoning: Advancing data distillation for competitive coding, 2025
Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar, Aleksander Ficek, Siddhartha Jain, Jocelyn Huang, Vahid Noroozi, and Boris Ginsburg · 2025
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G1: Bootstrapping perception and reasoning abilities of vision-language model via reinforcement learning, 2025
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Cited alongside, same era.
Evolving curricula with regret-based environment design
Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, and Tim Rocktäschel · 2022
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al · 2022
Cited alongside, same era.
Momentum-based weight interpolation of strong zero-shot models for continual learning
Zafir Stojanovski, Karsten Roth, and Zeynep Akata · 2022
Cited alongside, same era.
Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, , and Jason Wei · 2022
Cited alongside, same era.
Will we run out of data? limits of llm scaling based on human-generated data
Pablo Villalobos, Anson Ho, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, and Marius Hobbhahn · 2022
Cited alongside, same era.
Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, and Ludwig Schmidt · 2022
Cited alongside, same era.
Domain generalization: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2022
Cited alongside, same era.
Pug: Photorealistic and semantically controllable synthetic data for representation learning, 2023
Florian Bordes, Shashank Shekhar, Mark Ibrahim, Diane Bouchacourt, Pascal Vincent, and Ari S. Morcos · 2023
Cited alongside, same era.
Liang Chen, Hongcheng Gao, Tianyu Liu, Zhiqi Huang, Flood Sung, Xinyu Zhou, Yuxin Wu, and Baobao Chang · 2025
Closest in time.
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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Leon Guertler, Bobby Cheng, Simon Yu, Bo Liu, Leshem Choshen, and Cheston Tan · 2025
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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
Closest in time.
Reasoning core: A scalable rl environment for llm symbolic reasoning, 2025
Valentin Lacombe, Valentin Quesnel, , and Damien Sileo · 2025
Closest in time.
Zebralogic: On the scaling limits of llms for logical reasoning, 2025
Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, Ashish Sabharwal, Radha Poovendran, Peter Clark, and Yejin Choi · 2025
Closest in time.
SYNTHETIC-1: Scaling distributed synthetic data generation for verified reasoning, February 2025
Justus Mattern, Manveer, Jannik, Matthew, Felix, Johannes, and Vincent · 2025
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Ivan Moshkov, Darragh Hanley, Ivan Sorokin, Shubham Toshniwal, Christof Henkel, Benedikt Schifferer, Wei Du, and Igor Gitman · 2025
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Openai o3 and o4-mini system card
OpenAI · 2025
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BALROG: Benchmarking agentic LLM and VLM reasoning on games
Davide Paglieri, Bartłomiej Cupiał, Samuel Coward, Ulyana Piterbarg, Maciej Wolczyk, Akbir Khan, Eduardo Pignatelli, Łukasz Kuciński, Lerrel Pinto, Rob Fergus, Jakob Nicolaus Foerster, Jack Parker-Holder, and Tim Rocktäschel · 2025
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Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhang, Chen Bo Calvin Zhang, Mohamed Shaaban, John Ling, Sean Shi, et al · 2025
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Vgrp-bench: Visual grid reasoning puzzle benchmark for large vision-language models, 2025
Yufan Ren, Konstantinos Tertikas, Shalini Maiti, Junlin Han, Tong Zhang, Sabine Süsstrunk, and Filippos Kokkinos · 2025
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Lmact: A benchmark for in-context imitation learning with long multimodal demonstrations, 2025
Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li, Volodymyr Mnih, and Tim Genewein · 2025
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Korgym: A dynamic game platform for llm reasoning evaluation, 2025
ByteDance Seed, M-A-P, and Beihang University · 2025
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Sudoku-bench: Evaluating creative reasoning with sudoku variants, 2025
Jeffrey Seely, Yuki Imajuku, Tianyu Zhao, Edoardo Cetin, and Llion Jones · 2025
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Spurious rewards: Rethinking training signals in rlvr
Rulin Shao, Shuyue Stella Li, Rui Xin, Scott Geng, Yiping Wang, Sewoong Oh, Simon Shaolei Du, Nathan Lambert, Sewon Min, Ranjay Krishna, Yulia Tsvetkov, Hannaneh Hajishirzi, Pang Wei Koh, and Luke Zettlemoyer · 2025
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The leaderboard illusion, 2025
Shivalika Singh, Yiyang Nan, Alex Wang, Daniel D’Souza, Sayash Kapoor, Ahmet Üstün, Sanmi Koyejo, Yuntian Deng, Shayne Longpre, Noah A. Smith, Beyza Ermis, Marzieh Fadaee, and Sara Hooker · 2025
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Crossing the reward bridge: Expanding rl with verifiable rewards across diverse domains
Yi Su, Dian Yu, Linfeng Song, Juntao Li, Haitao Mi, Zhaopeng Tu, Min Zhang, and Dong Yu · 2025
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Logic-rl: Unleashing llm reasoning with rule-based reinforcement learning, 2025
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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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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Reinforcing multi-turn reasoning in llm agents via turn-level credit assignment
Siliang Zeng, Quan Wei, William Brown, Oana Frunza, Yuriy Nevmyvaka, and Mingyi Hong · 2025
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Videogamebench: Can vision-language models complete popular video games?, 2025
Alex L. Zhang, Thomas L. Griffiths, Karthik R. Narasimhan, and Ofir Press · 2025
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Qin Zhu, Fei Huang, Runyu Peng, Keming Lu, Bowen Yu, Qinyuan Cheng, Xipeng Qiu, Xuanjing Huang, and Junyang Lin · 2025
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