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Preference-based reinforcement learning (RL) provides a framework to train AI agents using human feedback through preferences over pairs of behaviors, enabling agents to learn desired behaviors when it is difficult to specify a numerical reward function.
Estimating the dimension of a model
Gideon Schwarz · 1978
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The Wisdom of Crowds
James Surowiecki · 2005
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Pairwise ranking aggregation in a crowdsourced setting
Xi Chen, Paul N. Bennett, Kevyn Collins-Thompson, and Eric Horvitz · 2013
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Ranking and combining multiple predictors without labeled data
Fabio Parisi, Francesco Strino, Boaz Nadler, and Yuval Kluger · 2014
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Crowdsourced data management: A survey
Guoliang Li, Jiannan Wang, Yudian Zheng, and Michael J. Franklin · 2016
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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A solution to the single-question crowd wisdom problem
Dražen Prelec, H. Sebastian Seung, and John McCoy · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy P. Lillicrap, and Martin A. Riedmiller · 2018
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Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations
Daniel Brown, Wonjoon Goo, Prabhat Nagarajan, and Scott Niekum · 2019
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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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Stable-baselines3: Reliable reinforcement learning implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann · 2021
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Learning reward functions from scale feedback
Nils Wilde, Erdem Biyik, Dorsa Sadigh, and Stephen L. Smith · 2021
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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, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan · 2022
Distributional preference learning: Understanding and accounting for hidden context in RLHF
Anand Siththaranjan, Cassidy Laidlaw, and Dylan Hadfield-Menell · 2023
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Breadcrumbs to the goal: Goal-conditioned exploration from human-in-the-loop feedback
Marcel Torne, Max Balsells, Zihan Wang, Samedh Desai, Tao Chen, Pulkit Agrawal, and Abhishek Gupta · 2023
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Batch reinforcement learning from crowds
Guoxi Zhang and Hisashi Kashima · 2023
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Maxmin-RLHF: Alignment with diverse human preferences
Souradip Chakraborty, Jiahao Qiu, Hui Yuan, Alec Koppel, Dinesh Manocha, Furong Huang, Amrit Bedi, and Mengdi Wang · 2024
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Towards measuring the representation of subjective global opinions in language models, 2024
Esin Durmus, Karina Nguyen, Thomas I. Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, Liane Lovitt, Sam McCandlish, Orowa Sikder, Alex Tamkin, Janel Thamkul, Jared Kaplan, Jack Clark, and Deep Ganguli · 2024
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Webgpt: Browser-assisted question-answering with human feedback, 2022
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll 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 F Christiano, Jan Leike, and Ryan Lowe · 2022
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Open problems and fundamental limitations of reinforcement learning from human feedback
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, et al · 2023
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A toolkit for reliable benchmarking and research in multi-objective reinforcement learning
Florian Felten, Lucas N. Alegre, Ann Nowé, Ana L. C. Bazzan, El Ghazali Talbi, Grégoire Danoy, and Bruno C. da Silva · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2023
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B-pref: Benchmarking preference-based reinforcement learning
Kimin Lee, Laura Smith, Anca Dragan, and Pieter Abbeel
Cited in the paper.
PEBBLE: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training
Kimin Lee, Laura M Smith, and Pieter Abbeel
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Personalizing reinforcement learning from human feedback with variational preference learning
Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, and Natasha Jaques · 2024
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Group robust preference optimization in reward-free rlhf
Shyam Sundhar Ramesh, Yifan Hu, Iason Chaimalas, Viraj Mehta, and Pier Giuseppe Sessa · 2024
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Group preference optimization: Few-shot alignment of large language models
Siyan Zhao, John Dang, and Aditya Grover · 2024
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No preference left behind: Group distributional preference optimization
Binwei Yao, Zefan Cai, Yun-Shiuan Chuang, Shanglin Yang, Ming Jiang, Diyi Yang, and Junjie Hu · 2025
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