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Large Language Models (LLMs) can acquire extensive world knowledge through pre-training on large corpora.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
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Policy gradient methods for reinforcement learning with function approximation
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Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Weiwei Cheng, and Eyke Hüllermeier · 2014
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Contextual dueling bandits
Miroslav Dudík, Katja Hofmann, Robert E Schapire, Aleksandrs Slivkins, and Masrour Zoghi · 2015
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Asynchronous methods for deep reinforcement learning
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Active preference-based learning of reward functions
Dorsa Sadigh, Anca D Dragan, Shankar Sastry, and Sanjit A Seshia · 2017
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Proximal policy optimization algorithms
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Tl; dr: Mining reddit to learn automatic summarization
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Learning dynamic robot-to-human object handover from human feedback
Andras Kupcsik, David Hsu, and Wee Sun Lee · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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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
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Tor Lattimore and Csaba Szepesvári · 2020
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Jun Xu, Zeng Wei, Long Xia, Yanyan Lan, Dawei Yin, Xueqi Cheng, and Ji-Rong Wen · 2020
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A reduction-based framework for conservative bandits and reinforcement learning
Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, and Simon S Du · 2021
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trlX: A scalable framework for RLHF, June 2023
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