| Toolformer: Language models can teach themselves to use tools T Schick, J Dwivedi-Yu, R Dessì, R Raileanu, M Lomeli, E Hambro, ... Advances in Neural Information Processing Systems 36, 2024 | 731 | 2024 |
| Augmented language models: a survey G Mialon, R Dessì, M Lomeli, C Nalmpantis, R Pasunuru, R Raileanu, ... arXiv preprint arXiv:2302.07842, 2023 | 293 | 2023 |
| Modeling others using oneself in multi-agent reinforcement learning R Raileanu, E Denton, A Szlam, R Fergus International conference on machine learning, 4257-4266, 2018 | 221 | 2018 |
| Ride: Rewarding impact-driven exploration for procedurally-generated environments R Raileanu, T Rocktäschel arXiv preprint arXiv:2002.12292, 2020 | 179 | 2020 |
| Challenges and applications of large language models J Kaddour, J Harris, M Mozes, H Bradley, R Raileanu, R McHardy arXiv preprint arXiv:2307.10169, 2023 | 164 | 2023 |
| Superbubbles in the Multiphase ISM and the Loading of Galactic Winds CG Kim, EC Ostriker, R Raileanu The Astrophysical Journal 834 (1), 25, 2016 | 162 | 2016 |
| Open-ended learning leads to generally capable agents OEL Team, A Stooke, A Mahajan, C Barros, C Deck, J Bauer, J Sygnowski, ... arXiv preprint arXiv:2107.12808, 2021 | 144 | 2021 |
| The nethack learning environment H Küttler, N Nardelli, A Miller, R Raileanu, M Selvatici, E Grefenstette, ... Advances in Neural Information Processing Systems 33, 7671-7684, 2020 | 141 | 2020 |
| Learning with amigo: Adversarially motivated intrinsic goals A Campero, R Raileanu, H Küttler, JB Tenenbaum, T Rocktäschel, ... arXiv preprint arXiv:2006.12122, 2020 | 136 | 2020 |
| Automatic data augmentation for generalization in deep reinforcement learning R Raileanu, M Goldstein, D Yarats, I Kostrikov, R Fergus arXiv preprint arXiv:2006.12862, 2020 | 102 | 2020 |
| Chain-of-verification reduces hallucination in large language models S Dhuliawala, M Komeili, J Xu, R Raileanu, X Li, A Celikyilmaz, J Weston arXiv preprint arXiv:2309.11495, 2023 | 96 | 2023 |
| Automatic data augmentation for generalization in reinforcement learning R Raileanu, M Goldstein, D Yarats, I Kostrikov, R Fergus Advances in Neural Information Processing Systems 34, 5402-5415, 2021 | 91 | 2021 |
| Decoupling value and policy for generalization in reinforcement learning R Raileanu, R Fergus International Conference on Machine Learning, 8787-8798, 2021 | 90 | 2021 |
| Improving intrinsic exploration with language abstractions J Mu, V Zhong, R Raileanu, M Jiang, N Goodman, T Rocktäschel, ... Advances in Neural Information Processing Systems 35, 33947-33960, 2022 | 47 | 2022 |
| Backplay:" man muss immer umkehren" C Resnick, R Raileanu, S Kapoor, A Peysakhovich, K Cho, J Bruna arXiv preprint arXiv:1807.06919, 2018 | 41 | 2018 |
| Fast adaptation to new environments via policy-dynamics value functions R Raileanu, M Goldstein, A Szlam, R Fergus Proceedings of the 37th International Conference on Machine Learning, 7920-7931, 2020 | 24 | 2020 |
| Exploration via elliptical episodic bonuses M Henaff, R Raileanu, M Jiang, T Rocktäschel Advances in Neural Information Processing Systems 35, 37631-37646, 2022 | 23 | 2022 |
| Toolformer: language models can teach themselves to use tools. 2023 T Schick, J Dwivedi-Yu, R Dessì, R Raileanu, M Lomeli, L Zettlemoyer, ... arXiv preprint arXiv:2302.04761, 2023 | 18 | 2023 |
| Insights from the neurips 2021 nethack challenge E Hambro, S Mohanty, D Babaev, M Byeon, D Chakraborty, ... NeurIPS 2021 Competitions and Demonstrations Track, 41-52, 2022 | 17 | 2022 |
| Building a subspace of policies for scalable continual learning JB Gaya, T Doan, L Caccia, L Soulier, L Denoyer, R Raileanu arXiv preprint arXiv:2211.10445, 2022 | 16 | 2022 |
Tim RocktäschelProfessor of Artificial Intelligence at UCL, Open-Endedness Team Lead at Google DeepMindVerified email at cs.ucl.ac.uk
