| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2510 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 989 | 2024 |
| Entity abstraction in visual model-based reinforcement learning R Veerapaneni, JD Co-Reyes, M Chang, M Janner, C Finn, J Wu, ... Conference on Robot Learning, 1439-1456, 2020 | 226 | 2020 |
| Ex2: Exploration with exemplar models for deep reinforcement learning J Fu, JD Co-Reyes, S Levine NeurIPS, spotlight, 2017 | 190 | 2017 |
| Self-consistent trajectory autoencoder: Hierarchical reinforcement learning with trajectory embeddings J Co-Reyes, YX Liu, A Gupta, B Eysenbach, P Abbeel, S Levine International conference on machine learning, 1009-1018, 2018 | 186 | 2018 |
| Evolving reinforcement learning algorithms JD Co-Reyes, Y Miao, D Peng, E Real, S Levine, QV Le, H Lee, A Faust International Conference on Learning Representations, oral presentation, 2021 | 97 | 2021 |
| Beyond human data: Scaling self-training for problem-solving with language models A Singh, JD Co-Reyes, R Agarwal, A Anand, P Patil, X Garcia, PJ Liu, ... arXiv preprint arXiv:2312.06585, 2023 | 87 | 2023 |
| Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research C Gulino, J Fu, W Luo, G Tucker, E Bronstein, Y Lu, J Harb, X Pan, ... Advances in Neural Information Processing Systems 36, 2024 | 86 | 2024 |
| Guiding policies with language via meta-learning JD Co-Reyes, A Gupta, S Sanjeev, N Altieri, J Andreas, J DeNero, ... International Conference on Learning Representations, 2018 | 73 | 2018 |
| Many-shot in-context learning R Agarwal, A Singh, LM Zhang, B Bohnet, L Rosias, S Chan, B Zhang, ... arXiv preprint arXiv:2404.11018, 2024 | 59 | 2024 |
| Small-scale proxies for large-scale transformer training instabilities M Wortsman, PJ Liu, L Xiao, K Everett, A Alemi, B Adlam, JD Co-Reyes, ... arXiv preprint arXiv:2309.14322, 2023 | 52 | 2023 |
| Training language models to self-correct via reinforcement learning A Kumar, V Zhuang, R Agarwal, Y Su, JD Co-Reyes, A Singh, K Baumli, ... arXiv preprint arXiv:2409.12917, 2024 | 43 | 2024 |
| Ecological reinforcement learning JD Co-Reyes, S Sanjeev, G Berseth, A Gupta, S Levine arXiv preprint arXiv:2006.12478, 2020 | 34 | 2020 |
| Improving large language model fine-tuning for solving math problems Y Liu, A Singh, CD Freeman, JD Co-Reyes, PJ Liu arXiv preprint arXiv:2310.10047, 2023 | 33 | 2023 |
| Azade Nova, John D Co-Reyes, Eric Chu, et al. Many-shot in-context learning R Agarwal, A Singh, LM Zhang, B Bohnet, S Chan, A Anand, Z Abbas arXiv preprint arXiv:2404.11018, 2024 | 23 | 2024 |
| Meta-learning language-guided policy learning JD Co-Reyes, A Gupta, S Sanjeev, N Altieri, J DeNero, P Abbeel, ... International Conference on Learning Representations 3, 2019 | 22 | 2019 |
| Information is power: Intrinsic control via information capture N Rhinehart, J Wang, G Berseth, J Co-Reyes, D Hafner, C Finn, S Levine Advances in Neural Information Processing Systems 34, 10745-10758, 2021 | 11 | 2021 |
| RL-DARTS: differentiable architecture search for reinforcement learning Y Miao, X Song, D Peng, S Yue, JD Co-Reyes, E Brevdo, A Faust | 9 | 2021 |
| Small-scale proxies for large-scale transformer training instabilities. Sep 2023 M Wortsman, PJ Liu, L Xiao, K Everett, A Alemi, B Adlam, JD Co-Reyes, ... URL http://arxiv. org/abs/2309.14322 v2, 0 | 7 | |
| Training language models to self-correct via reinforcement learning, 2024 A Kumar, V Zhuang, R Agarwal, Y Su, JD Co-Reyes, A Singh, K Baumli, ... URL https://arxiv. org/abs/2409.12917, 0 | 7 | |
Sergey LevineUC Berkeley, Physical IntelligenceVerified email at eecs.berkeley.edu
