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In this study, we aim to enhance the arithmetic reasoning ability of Large Language Models (LLMs) through zero-shot prompt optimization.
Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 1911
Earlier work this paper cites.
Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1988
Earlier work this paper cites.
Learning from demonstration
Stefan Schaal · 1996
Earlier work this paper cites.
Robot learning from demonstration
Christopher G Atkeson and Stefan Schaal · 1997
Earlier work this paper cites.
Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart Russell, et al · 2000
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
Earlier work this paper cites.
Xgboost: extreme gradient boosting
Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, Kailong Chen, Rory Mitchell, Ignacio Cano, Tianyi Zhou, et al · 2015
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Earlier work this paper cites.
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Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
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John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
Earlier work this paper cites.
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Earlier work this paper cites.
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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
Earlier work this paper cites.
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Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 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.
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Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Earlier work this paper cites.
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Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
Earlier work this paper cites.
Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Earlier work this paper cites.
Deep imitation learning for complex manipulation tasks from virtual reality teleoperation
Tianhao Zhang, Zoe McCarthy, Owen Jow, Dennis Lee, Xi Chen, Ken Goldberg, and Pieter Abbeel · 2018
Earlier work this paper cites.
Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations
Daniel Brown, Wonjoon Goo, Prabhat Nagarajan, and Scott Niekum · 2019
Earlier work this paper cites.
Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, George Tucker, and Sergey Levine · 2019
Earlier work this paper cites.
Policy continuation with hindsight inverse dynamics
Hao Sun, Zhizhong Li, Xiaotong Liu, Bolei Zhou, and Dahua Lin · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
An optimistic perspective on offline reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2020
Earlier work this paper cites.
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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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Alihan Hüyük, William R Zame, and Mihaela van der Schaar · 2022
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