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Reward-based finetuning is crucial for aligning language policies with intended behaviors (e.g., creativity and safety).
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
A survey of multi-objective sequential decision-making
Diederik M Roijers, Peter Vamplew, Shimon Whiteson, and Richard Dazeley · 2013
Earlier work this paper cites.
Multi-objective decision-theoretic planning
Diederik M Roijers · 2016
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Tl; dr: Mining reddit to learn automatic summarization
Michael Völske, Martin Potthast, Shahbaz Syed, and Benno Stein · 2017
Earlier work this paper cites.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata · 2018
Earlier work this paper cites.
Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern · 2018
Earlier work this paper cites.
Learning to extract coherent summary via deep reinforcement learning
Yuxiang Wu and Baotian Hu · 2018
Earlier work this paper cites.
Autosem: Automatic task selection and mixing in multi-task learning
Han Guo, Ramakanth Pasunuru, and Mohit Bansal · 2019
Earlier work this paper cites.
Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
Earlier work this paper cites.
You only train once: Loss-conditional training of deep networks
Alexey Dosovitskiy and Josip Djolonga · 2020
Earlier work this paper cites.
Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin · 2020
Earlier work this paper cites.
Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
Earlier work this paper cites.
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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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Learning neural network subspaces
Mitchell Wortsman, Maxwell C Horton, Carlos Guestrin, Ali Farhadi, and Mohammad Rastegari · 2021
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Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
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Learning a subspace of policies for online adaptation in reinforcement learning
Jean-Baptiste Gaya, Laure Soulier, and Ludovic Denoyer · 2022
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Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Alexandre Ramé, Guillaume Couairon, Corentin Dancette, Jean-Baptiste Gaya, Mustafa Shukor, Laure Soulier, and Matthieu Cord · 2023
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Scaling up models and data with t5x and seqio
Adam Roberts, Hyung Won Chung, Gaurav Mishra, Anselm Levskaya, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, et al · 2023
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Factually consistent summarization via reinforcement learning with textual entailment feedback
Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Léonard Hussenot, Orgad Keller, et al · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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A practical guide to multi-objective reinforcement learning and planning
Conor F Hayes, Roxana Rădulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M Zintgraf, Richard Dazeley, Fredrik Heintz, et al · 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, et al · 2022
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Diverse weight averaging for out-of-distribution generalization
Alexandre Rame, Matthieu Kirchmeyer, Thibaud Rahier, Alain Rakotomamonjy, Patrick Gallinari, and Matthieu Cord · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Pareto manifold learning: Tackling multiple tasks via ensembles of single-task models
Nikolaos Dimitriadis, Pascal Frossard, and François Fleuret · 2023
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Helping or herding? reward model ensembles mitigate but do not eliminate reward hacking
Jacob Eisenstein, Chirag Nagpal, Alekh Agarwal, Ahmad Beirami, Alex D’Amour, DJ Dvijotham, Adam Fisch, Katherine Heller, Stephen Pfohl, Deepak Ramachandran, et al · 2023
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Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Qi Liu, Tianyu Liu, and Zhifang Sui · 2023
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Adaptive budget allocation for parameter-efficient fine-tuning
Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao · 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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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2024
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Controllable preference optimization: Toward controllable multi-objective alignment
Yiju Guo, Ganqu Cui, Lifan Yuan, Ning Ding, Jiexin Wang, Huimin Chen, Bowen Sun, Ruobing Xie, Jie Zhou, Yankai Lin, Zhiyuan Liu, and Maosong Sun · 2024
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Putting rl back in rlhf
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Decoding-time realignment of language models
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Decoding-time language model alignment with multiple objectives
Ruizhe Shi, Yifang Chen, Yushi Hu, ALisa Liu, Noah Smith, Hannaneh Hajishirzi, and Simon Du · 2024
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Position: A roadmap to pluralistic alignment
Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell L Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, and Yejin Choi · 2024
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Rui Yang, Xiaoman Pan, Feng Luo, Shuang Qiu, Han Zhong, Dong Yu, and Jianshu Chen · 2024
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Panacea: Pareto alignment via preference adaptation for llms
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