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Recent advancements in test time compute, particularly through the use of verifier models, have significantly enhanced the reasoning capabilities of Large Language Models (LLMs).
Actor-critic algorithms
Vijay R. Konda and John N. Tsitsiklis · 1999
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Conservative q-learning for offline reinforcement learning, 2020
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2006
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Christopher Maddison, Arthur Guez, Laurent Sifre, George Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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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, 2021
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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Lora: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Earlier work this paper cites.
Offline reinforcement learning with implicit q-learning, 2021
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
Cited alongside, same era.
When should we prefer offline reinforcement learning over behavioral cloning?, 2022
Aviral Kumar, Joey Hong, Anikait Singh, and Sergey Levine · 2022
Cited alongside, same era.
Solving math word problems with process- and outcome-based feedback, 2022
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins · 2022
Cited alongside, same era.
Chai: A chatbot ai for task-oriented dialogue with offline reinforcement learning, 2022
Siddharth Verma, Justin Fu, Mengjiao Yang, and Sergey Levine · 2022
Cited alongside, same era.
Let’s verify step by step, 2023
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe · 2023
Cited alongside, same era.
Archer: Training language model agents via hierarchical multi-turn rl, 2024
Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, and Aviral Kumar · 2023
Later among the works it cites.
Alphamath almost zero: process supervision without process, 2024
Guoxin Chen, Minpeng Liao, Chengxi Li, and Kai Fan · 2024
Closest in time.
Learning to reason with llms
OpenAI · 2024
Closest in time.
Scaling llm test-time compute optimally can be more effective than scaling model parameters, 2024
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
Closest in time.
Metamath: Bootstrap your own mathematical questions for large language models, 2024
Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu · 2024
Closest in time.
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Offline rl for natural language generation with implicit language q learning, 2023
Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, and Sergey Levine · 2023
Cited alongside, same era.
Q*: Improving multi-step reasoning for llms with deliberative planning, 2024a
Chaojie Wang, Yanchen Deng, Zhiyi Lyu, Liang Zeng, Jujie He, Shuicheng Yan, and Bo An
Cited in the paper.
Math-shepherd: Verify and reinforce llms step-by-step without human annotations, 2024b
Peiyi Wang, Lei Li, Zhihong Shao, R. X. Xu, Damai Dai, Yifei Li, Deli Chen, Y. Wu, and Zhifang Sui
Cited in the paper.
Di Zhang, Xiaoshui Huang, Dongzhan Zhou, Yuqiang Li, and Wanli Ouyang
Cited in the paper.
Tinyllama: An open-source small language model, 2024b
Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, and Wei Lu
Cited in the paper.