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Awesome In-Context RL: A curated list of In-Context Reinforcement Learning - - —
| Date | Stars |
|---|---|
| 2026-07-31 | 305 |
| 2026-08-01 | 305 |
| 2026-08-02 | 305 |
| 2026-08-05 | 306 |
| 2026-08-06 | 306 |
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# Awesome In-Context Reinforcement Learning This is a collection of research papers for In-Context Reinforcement Learning (ICRL). The repository shall be regularly updated to track the frontiers. _Curated by dunnolab._ ----- Please, feel free to [PR](https://github.com/dunnolab/awesome-in-context-rl/pulls) new papers and resources you believe are relevant and awesome. ``` format: - [title](paper link) - author1, author2, and author3... ``` ## Papers ### 2025 - [In-Context Reinforcement Learning via Communicative World Models](https://arxiv.org/abs/2508.06659) - Fernando Martinez-Lopez, Tao Li, Yingdong Lu, Juntao Chen - [Reward Is Enough: LLMs Are In-Context Reinforcement Learners](https://arxiv.org/abs/2506.06303) - Kefan Song, Amir Moeini, Peng Wang, Lei Gong, Rohan Chandra, Yanjun Qi, Shangtong Zhang - [Filtering Learning Histories Enhances In-Context Reinforcement Learning](https://arxiv.org/pdf/2505.15143) - Weiqin Chen, Xinjie Zhang, Dharmashankar Subramanian, Santiago Paternain - [OmniRL: In-Context Reinforcement Learning by Large-Scale Meta-Training in Randomized Worlds](https://arxiv.org/abs/2502.02869) - Wang, Fan, Pengtao Shao, Yiming Zhang, Bo Yu, Shaoshan Liu, Ning Ding, Yang Cao, Yu Kang, and Haifeng Wang - [A **Survey** of In-Context Reinforcement Learning](https://arxiv.org/abs/2502.07978) - Amir Moeini, Jiuqi Wang, Jacob Beck, Ethan Blaser, Shimon Whiteson, Rohan Chandra, Shangtong Zhang - [Yes, Q-learning Helps Offline In-Context RL](https://arxiv.org/abs/2502.17666) - Denis Tarasov, Alexander Nikulin, Ilya Zisman, Albina Klepach, Andrei Polubarov, Nikita Lyubaykin, Alexander Derevyagin, Igor Kiselev, Vladislav Kurenkov - [Vintix: Action Model via In-Context Reinforcement Learning](https://arxiv.org/abs/2501.19400) - Andrey Polubarov, Nikita Lyubaykin, Alexander Derevyagin, Ilya Zisman, Denis Tarasov, Alexander Nikulin, Vladislav Kurenkov - [Training a Generally Curious Agent](https://arxiv.org/abs/2502.17543) - Fahim Tajwar, Yiding Jiang, Abitha Thankaraj, Sumaita Sadia Rahman, J Zico Kolter, Jeff Schneider, Ruslan Salakhutdinov ### 2024 - [LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations](https://arxiv.org/abs/2412.01441) - Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li, Volodymyr Mnih, Tim Genewein - [Meta-Reinforcement Learning Robust to Distributional Shift Via Performing Lifelong In-Context Learning](https://proceedings.mlr.press/v235/xu24o.html) - Tengye Xu, Zihao Li, Qinyuan Ren - [AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers](https://arxiv.org/abs/2411.11188) - Jake Grigsby, Justin Sasek, Samyak Parajuli, Daniel Adebi, Amy Zhang, Yuke Zhu - [LLMs Are In-Context Reinforcement Learners](https://arxiv.org/abs/2410.05362) - Giovanni Monea, Antoine Bosselut, Kianté Brantley, Yoav Artzi - [EVOLvE: Evaluating and Optimizing LLMs For Exploration](https://arxiv.org/abs/2410.06238) - Allen Nie, Yi Su, Bo Chang, Jonathan N. Lee, Ed H. Chi, Quoc V. Le, Minmin Chen - [Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models](https://arxiv.org/abs/2410.01280) - Can Demircan, Tankred Saanum, Akshay K. Jagadish, Marcel Binz, Eric Schulz - [ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AI](https://arxiv.org/abs/2410.02751) - Ahmad Elawady, Gunjan Chhablani, Ram Ramrakhya, Karmesh Yadav, Dhruv Batra, Zsolt Kira, Andrew Szot - [Retrieval-Augmented Decision Transformer: External Memory for In-context RL](https://arxiv.org/abs/2410.07071) - Thomas Schmied, Fabian Paischer, Vihang Patil, Markus Hofmarcher, Razvan Pascanu, Sepp Hochreiter - [Random Policy Enables In-Context Reinforcement Learning within Trust Horizons](https://arxiv.org/pdf/2410.19982) - Weiqin Chen, Santiago Paternain - [Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning](https://arxiv.org/abs/2406.00392) - Jonathan Cook, Chris Lu, Edw
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:90e75dc5afcb51fc, llm:Repository is a curated 'Awesome' list for In-Context Reinforcement Learning (topics: in-context-learning, in-context-reinforcement-learning, in-context-rl; description: 'Awesome In-Context RL: A curated list of In-Context Reinforcement Learning').
matched fp:90e75dc5afcb51fc, llm:Repository is a curated 'Awesome' list for In-Context Reinforcement Learning (topics: in-context-learning, in-context-reinforcement-learning, in-context-rl; description: 'Awesome In-Context RL: A curated list of In-Context Reinforcement Learning').