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The majority of language model training builds on imitation learning.
A theory of regularized markov decision processes
Matthieu Geist, Bruno Scherrer, and Olivier Pietquin · 1901
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A divergence minimization perspective on imitation learning methods
Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, and Shixiang Gu · 1911
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Imitation learning via off-policy distribution matching
Ilya Kostrikov, Ofir Nachum, and Jonathan Tompson · 1912
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Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1988
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A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams and David Zipser · 1989
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A framework for behavioural cloning
Michael Bain and Claude Sammut · 1995
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Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart J. Russell · 2000
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A game-theoretic approach to apprenticeship learning
Umar Syed and Robert E. Schapire · 2007
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Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, Anind K Dey, et al · 2008
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A survey of robot learning from demonstration
Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
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Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy
Brian D Ziebart · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Boosted and reward-regularized classification for apprenticeship learning
Bilal Piot, Matthieu Geist, and Olivier Pietquin · 2014
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Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and P. Abbeel · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Sequence level training with recurrent neural networks, 2016
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2016
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Watch this: Scalable cost-function learning for path planning in urban environments
Markus Wulfmeier, Dominic Zeng Wang, and Ingmar Posner · 2016
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Deep reinforcement learning from human preferences
Paul Christiano, Jan Leike, Tom B Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2017
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Imitation learning: A survey of learning methods
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne · 2017
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Bridging the gap between value and policy based reinforcement learning
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, and Dale Schuurmans · 2017
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Mutual alignment transfer learning
Markus Wulfmeier, Ingmar Posner, and P. Abbeel · 2017
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Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
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Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, and Laurent Charlin · 2018
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Shashi Narayan, Shay B. Cohen, and Mirella Lapata · 2018
Cited alongside, same era.
Texygen: A benchmarking platform for text generation models
Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu · 2018
Cited alongside, same era.
Araml: A stable adversarial training framework for text generation
Pei Ke, Fei Huang, Minlie Huang, and Xiaoyan Zhu · 2019
Cited alongside, same era.
Sqil: Imitation learning via reinforcement learning with sparse rewards, 2019
Siddharth Reddy, Anca D. Dragan, and Sergey Levine · 2019
Cited alongside, same era.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi · 2019
Open problems and fundamental limitations of reinforcement learning from human feedback
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, J’er’emy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco di Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, and Dylan Hadfield-Menell · 2023
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Introduction to latent variable energy-based models: A path towards autonomous machine intelligence
Anna Dawid and Yann LeCun · 2023
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Inverse preference learning: Preference-based rl without a reward function
Joey Hejna and Dorsa Sadigh · 2023
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Cited alongside, same era.
Autoregressive knowledge distillation through imitation learning
Alexander Lin, Jeremy Wohlwend, Howard Chen, and Tao Lei · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Error bounds of imitating policies and environments
Tian Xu, Ziniu Li, and Yang Yu · 2020
Cited alongside, same era.
Training verifiers to solve math word problems
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
Cited alongside, same era.
Fixing exposure bias with imitation learning needs powerful oracles
Luca Hormann and Artem Sokolov · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Textgail: Generative adversarial imitation learning for text generation
Qingyang Wu, Lei Li, and Zhou Yu · 2021
Cited alongside, same era.
Rebekka Hubert, Artem Sokolov, and Stefan Riezler · 2023
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
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A Survey of Reinforcement Learning from Human Feedback, 2023
Timo Kaufmann, Paul Weng, Viktor Bengs, and Eyke Hüllermeier · 2023
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Rlaif: Scaling reinforcement learning from human feedback with ai feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, and Sushant Prakash · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Exploring the limits of transfer learning with a unified text-to-text transformer, 2023
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2023
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Vanishing gradients in reinforcement finetuning of language models
Noam Razin, Hattie Zhou, Omid Saremi, Vimal Thilak, Arwen Bradley, Preetum Nakkiran, Joshua Susskind, and Etai Littwin · 2023
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Dual rl: Unification and new methods for reinforcement and imitation learning
Harshit Sikchi, Qinqing Zheng, Amy Zhang, and Scott Niekum · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Coherent soft imitation learning
Joe Watson, Sandy H. Huang, and Nicolas Heess · 2023
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Foundations for transfer in reinforcement learning: A taxonomy of knowledge modalities
Markus Wulfmeier, Arunkumar Byravan, Sarah Bechtle, Karol Hausman, and Nicolas Manfred Otto Heess · 2023
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On-policy distillation of language models: Learning from self-generated mistakes
Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, and Olivier Bachem · 2024
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The pitfalls of next-token prediction
Gregor Bachmann and Vaishnavh Nagarajan · 2024
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Massively scalable inverse reinforcement learning in google maps
Matt Barnes, Matthew Abueg, Oliver F. Lange, Matt Deeds, Jason Trader, Denali Molitor, Markus Wulfmeier, and Shawn O’Banion · 2024
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Lora learns less and forgets less, 2024
Dan Biderman, Jose Gonzalez Ortiz, Jacob Portes, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, and John P. Cunningham · 2024
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Sequencematch: Imitation learning for autoregressive sequence modelling with backtracking, 2024
Chris Cundy and Stefano Ermon · 2024
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Gemini: A family of highly capable multimodal models, 2024
Gemini Team et al · 2024
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From r to q*: Your language model is secretly a q-function, 2024
Rafael Rafailov, Joey Hejna, Ryan Park, and Chelsea Finn · 2024
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Hybrid inverse reinforcement learning
Juntao Ren, Gokul Swamy, Zhiwei Steven Wu, J. Andrew Bagnell, and Sanjiban Choudhury · 2024
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Supervised fine-tuning as inverse reinforcement learning
Hao Sun · 2024
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