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Offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
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Temporal credit assignment in reinforcement learning
Richard Stuart Sutton · 1984
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A training algorithm for optimal margin classifiers
Bernhard E Boser, Isabelle M Guyon, and Vladimir N Vapnik · 1992
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Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
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Are loss functions all the same?
Lorenzo Rosasco, Ernesto De Vito, Andrea Caponnetto, Michele Piana, and Alessandro Verri · 2004
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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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Learning word vectors for sentiment analysis
Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts · 2011
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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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Tl; dr: Mining reddit to learn automatic summarization
Michael Völske, Martin Potthast, Shahbaz Syed, and Benno Stein · 2017
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Evolved policy gradients
Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly Stadie, Filip Wolski, OpenAI Jonathan Ho, and Pieter Abbeel · 2018
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Implementation matters in deep rl: A case study on ppo and trpo
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 2019
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Way off-policy batch deep reinforcement learning of implicit human preferences in dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, and Rosalind Picard · 2019
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Improving generalization in meta reinforcement learning using learned objectives
Louis Kirsch, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Meta-learning curiosity algorithms
Ferran Alet, Martin F Schneider, Tomas Lozano-Perez, and Leslie Pack Kaelbling · 2020
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Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith · 2020
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Discovering reinforcement learning algorithms
Junhyuk Oh, Matteo Hessel, Wojciech M Czarnecki, Zhongwen Xu, Hado P van Hasselt, Satinder Singh, and David Silver · 2020
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Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Brenden K Petersen, Mikel Landajuela Larma, Terrell N Mundhenk, Claudio Prata Santiago, Soo Kyung Kim, and Joanne Taery Kim · 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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Neural symbolic regression that scales
Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, and Giambattista Parascandolo · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Evolving reinforcement learning algorithms
John D Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Sergey Levine, Quoc V Le, Honglak Lee, and Aleksandra Faust · 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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Symbolic regression via neural-guided genetic programming population seeding
T Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Claudio P Santiago, Daniel M Faissol, and Brenden K Petersen · 2021
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Neural laplace: Learning diverse classes of differential equations in the laplace domain
Samuel I Holt, Zhaozhi Qian, and Mihaela van der Schaar · 2022
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Discovered policy optimisation
Chris Lu, Jakub Kuba, Alistair Letcher, Luke Metz, Christian Schroeder de Witt, and Jakob Foerster · 2022
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Some things are more cringe than others: Preference optimization with the pairwise cringe loss
Jing Xu, Andrew Lee, Sainbayar Sukhbaatar, and Jason Weston · 2023
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Language to rewards for robotic skill synthesis
Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montse Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, et al · 2023
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Slic-hf: Sequence likelihood calibration with human feedback
Yao Zhao, Rishabh Joshi, Tianqi Liu, Misha Khalman, Mohammad Saleh, and Peter J Liu · 2023
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Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms
Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Ahmet Üstün, and Sara Hooker · 2024
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Luke Metz, James Harrison, C Daniel Freeman, Amil Merchant, Lucas Beyer, James Bradbury, Naman Agrawal, Ben Poole, Igor Mordatch, Adam Roberts, et al · 2022
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Solving math word problems with process-and outcome-based feedback
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins · 2022
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Model card and evaluations for claude models, 2023
Anthropic · 2023
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A general theoretical paradigm to understand learning from human preferences
Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, and Rémi Munos · 2023
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Ultrafeedback: Boosting language models with high-quality feedback
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun · 2023
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Alpacafarm: A simulation framework for methods that learn from human feedback, 2023
Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Gemini: A family of highly capable multimodal models, 2023
Google DeepMind Gemini-Team · 2023
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Carlo Alfano, Sebastian Towers, Silvia Sapora, Chris Lu, and Patrick Rebeschini · 2024
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Dense reward for free in reinforcement learning from human feedback
Alex J Chan, Hao Sun, Samuel Holt, and Mihaela van der Schaar · 2024
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Length-controlled alpacaeval: A simple way to debias automatic evaluators
Yann Dubois, Balázs Galambosi, Percy Liang, and Tatsunori B Hashimoto · 2024
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Kto: Model alignment as prospect theoretic optimization, 2024
Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela · 2024
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Gemma: Open models based on gemini research and technology
Gemma-Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al · 2024
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Direct language model alignment from online ai feedback
Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Rame, Thomas Mesnard, Yao Zhao, Bilal Piot, et al · 2024
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ODE discovery for longitudinal heterogeneous treatment effects inference
Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets, Zhaozhi Qian, and Mihaela van der Schaar · 2024
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Large language models as evolution strategies
Robert Tjarko Lange, Yingtao Tian, and Yujin Tang · 2024
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Large language models as in-context ai generators for quality-diversity
Bryan Lim, Manon Flageat, and Antoine Cully · 2024
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Evolution of heuristics: Towards efficient automatic algorithm design using large language model
Fei Liu, Tong Xialiang, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang · 2024
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Mathematical discoveries from program search with large language models
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, Pengming Wang, Omar Fawzi, et al · 2024
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Xingyou Song, Yingtao Tian, Robert Tjarko Lange, Chansoo Lee, Yujin Tang, and Yutian Chen · 2024
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A minimaximalist approach to reinforcement learning from human feedback
Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, and Alekh Agarwal · 2024
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Generalized preference optimization: A unified approach to offline alignment
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, and Bilal Piot · 2024
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Zephyr 7b gemma
Lewis Tunstall and Philipp Schmid · 2024
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Fine-grained human feedback gives better rewards for language model training
Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A Smith, Mari Ostendorf, and Hannaneh Hajishirzi · 2024
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2024
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Weak-to-strong search: Align large language models via searching over small language models
Zhanhui Zhou, Zhixuan Liu, Jie Liu, Zhichen Dong, Chao Yang, and Yu Qiao · 2024
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