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Foundation models excel at single-turn reasoning but struggle with multi-turn exploration in dynamic environments, a requirement for many real-world challenges.
Learning to learn: Introduction and overview
Sebastian Thrun and Lorien Pratt · 1998
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Mnemonic function of the dorsolateral prefrontal cortex in conflict-induced behavioral adjustment
Farshad A Mansouri, Mark J Buckley, and Keiji Tanaka · 2007
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Active Learning Literature Survey
Burr Settles · 2009
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MuJoCo: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Greg Brockman, Vicki Cheung, Theodore Hester, Jonas Pettersson, John Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Curiosity-driven Exploration by Self-supervised Prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
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Exploration by Random Network Distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2018
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Embodied Question Answering
Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra · 2018
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Quantitative assessment of prefrontal cortex in humans relative to nonhuman primates
Chad J Donahue, Matthew F Glasser, Todd M Preuss, James K Rilling, and David C Van Essen · 2018
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Yuval Tassa, Yossi Doron, Alistair Mulhern, Tom Erez, Yazhao Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Gianluca Barth-Maron, Matthew Hessel, et al · 2018
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Go-Explore: a New Approach for Hard-Exploration Problems
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2019
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Behaviour suite for reinforcement learning
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, and Hado Van Hasselt · 2019
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Never Give Up: Learning Directed Exploration Strategies
Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Bilal Piot, Steven Kapturowski, Olivier Tieleman, Martín Arjovsky, Alexander Pritzel, Andew Bolt, et al · 2020
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Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
Jane X Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charlie Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, Francis Song, et al · 2021
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Transformers generalize differently from information stored in context vs in weights
Stephanie C Y Chan, Ishita Dasgupta, Junkyung Kim, Dharshan Kumaran, Andrew K Lampinen, and Felix Hill · 2022
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BYOL-Explore: Exploration by Bootstrapped Prediction
Zhaohan Guo, Shantanu Thakoor, Miruna Pîslar, Bernardo Avila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, et al · 2022
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Semantic Exploration from Language Abstractions and Pretrained Representations
Allison Tam, Neil Rabinowitz, Andrew Lampinen, Nicholas A Roy, Stephanie Chan, DJ Strouse, Jane Wang, Andrea Banino, and Felix Hill · 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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Meta-in-context learning in large language models
Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matt Botvinick, Jane Wang, and Eric Schulz · 2023
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LLaMA Rider: Spurring Large Language Models to Explore the Open World
Yicheng Feng, Yuxuan Wang, Jiazheng Liu, Sipeng Zheng, and Zongqing Lu · 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
Cited alongside, same era.
Few-shot fine-tuning vs. in-context learning: A fair comparison and evaluation
Can large language models explore in-context?
Akshay Krishnamurthy, Keegan Harris, Dylan J Foster, Cyril Zhang, and Aleksandrs Slivkins · 2024
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OpenEQA: Embodied Question Answering in the Era of Foundation Models
Arjun Majumdar, Anurag Ajay, Xiaohan Zhang, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul Mcvay, Oleksandr Maksymets, Sergio Arnaud, et al · 2024
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BALROG: Benchmarking agentic LLM and VLM reasoning on games
Davide Paglieri, Bartłomiej Cupiał, Samuel Coward, Ulyana Piterbarg, Maciej Wolczyk, Akbir Khan, Eduardo Pignatelli, Łukasz Kuciński, Lerrel Pinto, Rob Fergus, Jakob Nicolaus Foerster, Jack Parker-Holder, and Tim Rocktäschel · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al · 2024
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Explore until Confident: Efficient Exploration for Embodied Question Answering
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Marius Mosbach, Tiago Pimentel, Shauli Ravfogel, Dietrich Klakow, and Yanai Elazar · 2023
Cited alongside, same era.
Mathematical discoveries from program search with large language models
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco J. R. Ruiz, Jordan S. Ellenberg, Pengming Wang, Omar Fawzi, Pushmeet Kohli, Alhussein Fawzi, Josh Grochow, Andrea Lodi, Jean-Baptiste Mouret, Talia Ringer, and Tao Yu · 2023
Cited alongside, same era.
Unlocking the Power of Representations in Long-term Novelty-based Exploration
Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, and Bilal Piot · 2023
Cited alongside, same era.
EXCALIBUR: Encouraging and Evaluating Embodied Exploration
Hao Zhu, Raghav Kapoor, So Yeon Min, Winson Han, Jiatai Li, Kaiwen Geng, Graham Neubig, Yonatan Bisk, Aniruddha Kembhavi, and Luca Weihs · 2023
Cited alongside, same era.
Toward understanding in-context vs. in-weight learning
Bryan Chan, Xinyi Chen, András György, and Dale Schuurmans · 2024
Cited alongside, same era.
NVLM: Open Frontier-Class Multimodal LLMs
Wenliang Dai, Nayeon Lee, Boxin Wang, Zhuoling Yang, Zihan Liu, Jon Barker, Tuomas Rintamaki, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping · 2024
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Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models
Matt Deitke, Christopher Clark, Sangho Lee, Rohun Tripathi, Yue Yang, Jae Sung Park, Mohammadreza Salehi, Niklas Muennighoff, Kyle Lo, Luca Soldaini, et al · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Allen Z Ren, Jaden Clark, Anushri Dixit, Masha Itkina, Anirudha Majumdar, and Dorsa Sadigh · 2024
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LMAct: A benchmark for in-context imitation learning with long multimodal demonstrations
Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li, Volodymyr Mnih, and Tim Genewein · 2024
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Scaling instructable agents across many simulated worlds
SIMA Team, Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, Stephanie C Y Chan, Jeff Clune, Adrian Collister, Vikki Copeman, Alex Cullum, Ishita Dasgupta, Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy, Martin Engelcke, Ryan Faulkner, Frankie Garcia, Charles Gbadamosi, Zhitao Gong, Lucy Gonzales, Kshitij Gupta, Karol Gregor, Arne Olav Hallingstad, Tim Harley, Sam Haves, Felix Hill, Ed Hirst, Drew A Hudson, Jony Hudson, Steph Hughes-Fitt, Danilo J Rezende, Mimi Jasarevic, Laura Kampis, Rosemary Ke, Thomas Keck, Junkyung Kim, Oscar Knagg, Kavya Kopparapu, Rory Lawton, Andrew Lampinen, Shane Legg, Alexander Lerchner, Marjorie Limont, Yulan Liu, Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons, Loic Matthey, Siobhan Mcloughlin, Piermaria Mendolicchio, Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valeria Oliveira, Yanko Oliveira, Hannah Openshaw, Renke Pan, Aneesh Pappu, Alex Platonov, Ollie Purkiss, David Reichert, John Reid, Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P Sawyer, Tim Scholtes, Guy Simmons, Daniel Slater, Hubert Soyer, Heiko Strathmann, Peter Stys, Allison C Tam, Denis Teplyashin, Tayfun Terzi, Davide Vercelli, Bojan Vujatovic, Marcus Wainwright, Jane X Wang, Zhengdong Wang, Daan Wierstra, Duncan Williams, Nathaniel Wong, Sarah York, and Nick Young · 2024
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Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study
Weihao Tan, Ziluo Ding, Wentao Zhang, Boyu Li, Bohan Zhou, Junpeng Yue, Haochong Xia, Jiechuan Jiang, Longtao Zheng, Xinrun Xu, et al · 2024
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Can LLMs learn from previous mistakes? investigating LLMs’ errors to boost for reasoning
Yongqi Tong, Dawei Li, Sizhe Wang, Yujia Wang, Fei Teng, and Jingbo Shang · 2024
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Solving olympiad geometry without human demonstrations
Trieu Trinh, Yuhuai Tony Wu, Quoc Le, He He, and Thang Luong · 2024
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Claude 3.7 Sonnet and Claude Code, 2025
Anthropic · 2025
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Gemini 2.5: Our most intelligent AI model, 2025
Google · 2025
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Is your LLM trapped in a mental set? investigative study on how mental sets affect the reasoning capabilities of LLMs
Saiful Haq, Niyati Chhaya, Piyush Pandey, and Pushpak Bhattacharya · 2025
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Hello GPT-4o, 2024
OpenAI · 2025
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OpenAI o3 and o4-mini system card
OpenAI · 2025
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Welcome to the era of experience
David Silver and Richard S Sutton · 2025
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VideoGameBench: Can vision-language models complete popular video games?
Alex L Zhang, Thomas L Griffiths, Karthik R Narasimhan, and Ofir Press · 2025
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