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We investigate the extent to which contemporary Large Language Models (LLMs) can engage in exploration, a core capability in reinforcement learning and decision making.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R. Thompson · 1933
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The nonstochastic multiarmed bandit problem
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Ranked bandits in metric spaces: Learning optimally diverse rankings over large document collections
Aleksandrs Slivkins, Filip Radlinski, and Sreenivas Gollapudi · 2010
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Analysis of Thompson Sampling for the multi-armed bandit problem
Shipra Agrawal and Navin Goyal · 2012
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Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
Sébastien Bubeck and Nicolo Cesa-Bianchi · 2012
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Thompson sampling: An asymptotically optimal finite-time analysis
Emilie Kaufmann, Nathaniel Korda, and Rémi Munos · 2012
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Near-optimal regret bounds for thompson sampling
Shipra Agrawal and Navin Goyal · 2013
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Adaptive contract design for crowdsourcing markets: Bandit algorithms for repeated principal-agent problems
Chien-Ju Ho, Aleksandrs Slivkins, and Jennifer Wortman Vaughan · 2014
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A tutorial on thompson sampling
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, and Zheng Wen · 2018
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Introduction to multi-armed bandits
Aleksandrs Slivkins · 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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Bandit Algorithms
Tor Lattimore and Csaba Szepesvári · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
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Bayesian decision-making under misspecified priors with applications to meta-learning
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J Hsu, Thodoris Lykouris, Miro Dudik, and Robert E Schapire · 2021
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma · 2021
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Do as I can, not as I say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, Daniel Herzon, Alexand Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan, and Andy Zeng · 2022
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What learning algorithm is in-context learning? Investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou · 2022
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What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant · 2022
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General-purpose in-context learning by meta-learning transformers
Louis Kirsch, James Harrison, Jascha Sohl-Dickstein, and Luke Metz · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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In-context reinforcement learning with algorithm distillation
Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, and Volodymyr Mnih · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou · 2022
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Prompting decision transformer for few-shot policy generalization
Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua Tenenbaum, and Chuang Gan · 2022
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A mechanism for sample-efficient in-context learning for sparse retrieval tasks
Jacob Abernethy, Alekh Agarwal, Teodor V Marinov, and Manfred K Warmuth · 2023
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Transformers learn to implement preconditioned gradient descent for in-context learning
Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, and Suvrit Sra · 2023
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In-context learning through the bayesian prism
Kabir Ahuja, Madhur Panwar, and Navin Goyal · 2023
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Transformers as statisticians: Provable in-context learning with in-context algorithm selection
Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, and Song Mei · 2023
Cited alongside, same era.
Bandit social learning: Exploration under myopic behavior
Kiarash Banihashem, MohammadTaghi Hajiaghayi, Suho Shin, and Aleksandrs Slivkins · 2023
Cited alongside, same era.
Understanding in-context learning in transformers and LLMs by learning to learn discrete functions
Satwik Bhattamishra, Arkil Patel, Phil Blunsom, and Varun Kanade · 2023
Cited alongside, same era.
Using cognitive psychology to understand GPT-3
Marcel Binz and Eric Schulz · 2023
Cited alongside, same era.
Large language models can implement policy iteration
Ethan Brooks, Logan A Walls, Richard Lewis, and Satinder Singh · 2023
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr · 2023
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Do pretrained transformers really learn in-context by gradient descent?
Lingfeng Shen, Aayush Mishra, and Daniel Khashabi · 2023
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
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Demonstrations are all you need: Advancing offensive content paraphrasing using in-context learning
Anirudh Som, Karan Sikka, Helen Gent, Ajay Divakaran, Andreas Kathol, and Dimitra Vergyri · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang · 2023
Cited alongside, same era.
Transformers implement functional gradient descent to learn non-linear functions in context
Xiang Cheng, Yuxin Chen, and Suvrit Sra · 2023
Cited alongside, same era.
Meta-in-context learning in large language models
Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matt Botvinick, Jane Wang, and Eric Schulz · 2023
Cited alongside, same era.
The case for 4-bit precision: k-bit inference scaling laws
Tim Dettmers and Luke Zettlemoyer · 2023
Cited alongside, same era.
Synergpt: In-context learning for personalized drug synergy prediction and drug design
Carl N Edwards, Aakanksha Naik, Tushar Khot, Martin D Burke, Heng Ji, and Tom Hope · 2023
Cited alongside, same era.
Deqing Fu, Tian-Qi Chen, Robin Jia, and Vatsal Sharan · 2023
Cited alongside, same era.
Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
Cited alongside, same era.
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, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom · 2023
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Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change
Karthik Valmeekam, Matthew Marquez, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati · 2023
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Transformers learn in-context by gradient descent
Johannes Von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov · 2023
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Voyager: An open-ended embodied agent with large language models
Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2023
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Lucas Weber, Elia Bruni, and Dieuwke Hupkes · 2023
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The learnability of in-context learning
Noam Wies, Yoav Levine, and Amnon Shashua · 2023
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How many pretraining tasks are needed for in-context learning of linear regression?
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, and Peter L Bartlett · 2023
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Creative robot tool use with large language models
Mengdi Xu, Peide Huang, Wenhao Yu, Shiqi Liu, Xilun Zhang, Yaru Niu, Tingnan Zhang, Fei Xia, Jie Tan, and Ding Zhao · 2023
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Eunice Yiu, Eliza Kosoy, and Alison Gopnik · 2023
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Skill-mix: A flexible and expandable family of evaluations for ai models
Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen, Anirudh Goyal, and Sanjeev Arora · 2023
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In-context language learning: Architectures and algorithms
Ekin Akyürek, Bailin Wang, Yoon Kim, and Jacob Andreas · 2024
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Survey on large language model-enhanced reinforcement learning: Concept, taxonomy, and methods
Yuji Cao, Huan Zhao, Yuheng Cheng, Ting Shu, Guolong Liu, Gaoqi Liang, Junhua Zhao, and Yun Li · 2024
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Cogbench: a large language model walks into a psychology lab
Julian Coda-Forno, Marcel Binz, Jane X Wang, and Eric Schulz · 2024
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Relative value biases in large language models
William M Hayes, Nicolas Yax, and Stefano Palminteri · 2024
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An information-theoretic analysis of in-context learning
Hong Jun Jeon, Jason D Lee, Qi Lei, and Benjamin Van Roy · 2024
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Exposing attention glitches with flip-flop language modeling
Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, and Cyril Zhang · 2024
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LLMs are in-context reinforcement learners
Giovanni Monea, Antoine Bosselut, Kiant’e Brantley, and Yoav Artzi · 2024
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EVOLvE: Evaluating and optimizing LLMs for exploration
Allen Nie, Yi Su, Jonathan N. Lee, Ed H. Chi, Quoc V. Le, and Chen Minmin · 2024
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Do LLM agents have regret? A case study in online learning and games
Chanwoo Park, Xiangyu Liu, Asuman Ozdaglar, and Kaiqing Zhang · 2024
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In-context learning agents are asymmetric belief updaters
Johannes A Schubert, Akshay K Jagadish, Marcel Binz, and Eric Schulz · 2024
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Smartplay: A benchmark for LLMs as intelligent agents
Yue Wu, Xuan Tang, Tom Mitchell, and Yuanzhi Li · 2024
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