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Reinforcement learning (RL) has emerged as a pivotal technique for fine-tuning large language models (LLMs) on specific tasks.
Cultural transmission and evolution: A quantitative approach
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Pareto multi objective optimization
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Training deep convolutional neural networks to play go
Christopher Clark and Amos Storkey · 2015
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Mastering the game of go without human knowledge
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Deep multi-agent reinforcement learning
J Foerster · 2018
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Richard S Sutton and Andrew G Barto · 2018
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Language models are few-shot learners
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Analyzing sentiment using imdb dataset
Sandesh Tripathi, Ritu Mehrotra, Vidushi Bansal, and Shweta Upadhyay · 2020
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An overview of multi-agent reinforcement learning from game theoretical perspective
Yaodong Yang and Jun Wang · 2020
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A general language assistant as a laboratory for alignment
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al · 2021
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Introduction to game theory
Fei Fang, Shutian Liu, Anjon Basak, Quanyan Zhu, Christopher D Kiekintveld, and Charles A Kamhoua · 2021
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Recursively summarizing books with human feedback
Jeff Wu, Long Ouyang, Daniel M Ziegler, Nisan Stiennon, Ryan Lowe, Jan Leike, and Paul Christiano · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al · 2022
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Human-level play in the game of diplomacy by combining language models with strategic reasoning
Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyuan Hu, et al · 2022
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Pre-trained language models for interactive decision-making
Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, et al · 2022
Understanding the effects of rlhf on llm generalisation and diversity
Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, and Roberta Raileanu · 2023
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Rltf: Reinforcement learning from unit test feedback
Jiate Liu, Yiqin Zhu, Kaiwen Xiao, QIANG FU, Xiao Han, Yang Wei, and Deheng Ye · 2023
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The importance of human-labeled data in the era of llms
Yang Liu · 2023
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A review of cooperative multi-agent deep reinforcement learning
Afshin Oroojlooy and Davood Hajinezhad · 2023
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Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
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Learning math reasoning from self-sampled correct and partially-correct solutions
Ansong Ni, Jeevana Priya Inala, Chenglong Wang, Oleksandr Polozov, Christopher Meek, Dragomir Radev, and Jianfeng Gao · 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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Rt-2: Vision-language-action models transfer web knowledge to robotic control
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al · 2023
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Palm-e: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al · 2023
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A social path to human-like artificial intelligence
Edgar A Duéñez-Guzmán, Suzanne Sadedin, Jane X Wang, Kevin R McKee, and Joel Z Leibo · 2023
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Asymmetric self-play-enabled intelligent heterogeneous multirobot catching system using deep multiagent reinforcement learning
Yuan Gao, Junfeng Chen, Xi Chen, Chongyang Wang, Junjie Hu, Fuqin Deng, and Tin Lun Lam · 2023
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Aligning language models with preferences through f-divergence minimization
Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Nahyeon Ryu, and Marc Dymetman · 2023
Cited alongside, same era.
Execution-based code generation using deep reinforcement learning
Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, and Chandan K Reddy · 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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Sequential cooperative multi-agent reinforcement learning
Yifan Zang, Jinmin He, Kai Li, Haobo Fu, Qiang Fu, and Junliang Xing · 2023
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Secrets of rlhf in large language models part i: Ppo
Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, et al · 2023
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Beavertails: Towards improved safety alignment of llm via a human-preference dataset
Jiaming Ji, Mickel Liu, Josef Dai, Xuehai Pan, Chi Zhang, Ce Bian, Boyuan Chen, Ruiyang Sun, Yizhou Wang, and Yaodong Yang · 2024
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Language model alignment with elastic reset
Michael Noukhovitch, Samuel Lavoie, Florian Strub, and Aaron C Courville · 2024
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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 · 2024
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Entropy-regularized token-level policy optimization for large language models
Muning Wen, Cheng Deng, Jun Wang, Weinan Zhang, and Ying Wen · 2024
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