| 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 | 2556 | 2023 |
| Gemma 2: Improving open language models at a practical size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv preprint arXiv:2408.00118, 2024 | 354 | 2024 |
| Acme: A research framework for distributed reinforcement learning MW Hoffman, B Shahriari, J Aslanides, G Barth-Maron, N Momchev, ... arXiv preprint arXiv:2006.00979, 2022 | 272 | 2022 |
| Leverage the average: an analysis of KL regularization in reinforcement learning N Vieillard, T Kozuno, B Scherrer, O Pietquin, R Munos, M Geist Advances in Neural Information Processing Systems 33, 12163-12174, 2020 | 137* | 2020 |
| On-policy distillation of language models: Learning from self-generated mistakes R Agarwal*, N Vieillard*, Y Zhou, P Stanczyk, SR Garea, M Geist, ... The Twelfth International Conference on Learning Representations, 2024 | 136* | 2024 |
| Munchausen reinforcement learning N Vieillard, O Pietquin, M Geist Advances in Neural Information Processing Systems 33, 4235-4246, 2020 | 114 | 2020 |
| Factually consistent summarization via reinforcement learning with textual entailment feedback P Roit, J Ferret, L Shani, R Aharoni, G Cideron, R Dadashi, M Geist, ... arXiv preprint arXiv:2306.00186, 2023 | 74 | 2023 |
| Offline reinforcement learning as anti-exploration S Rezaeifar, R Dadashi, N Vieillard, L Hussenot, O Bachem, O Pietquin, ... Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 8106-8114, 2022 | 62 | 2022 |
| Warm: On the benefits of weight averaged reward models A Ramé, N Vieillard, L Hussenot, R Dadashi, G Cideron, O Bachem, ... arXiv preprint arXiv:2401.12187, 2024 | 55 | 2024 |
| Offline reinforcement learning with pseudometric learning R Dadashi, S Rezaeifar, N Vieillard, L Hussenot, O Pietquin, M Geist International Conference on Machine Learning, 2307-2318, 2021 | 42 | 2021 |
| Momentum in reinforcement learning N Vieillard, B Scherrer, O Pietquin, M Geist International Conference on Artificial Intelligence and Statistics, 2529-2538, 2020 | 39 | 2020 |
| Deep conservative policy iteration N Vieillard, O Pietquin, M Geist Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6070-6077, 2020 | 32 | 2020 |
| Bond: Aligning llms with best-of-n distillation PG Sessa, R Dadashi, L Hussenot, J Ferret, N Vieillard, A Ramé, ... arXiv preprint arXiv:2407.14622, 2024 | 20 | 2024 |
| On connections between constrained optimization and reinforcement learning N Vieillard, O Pietquin, M Geist arXiv preprint arXiv:1910.08476, 2019 | 18 | 2019 |
| Implicitly regularized rl with implicit q-values N Vieillard, M Andrychowicz, A Raichuk, O Pietquin, M Geist arXiv preprint arXiv:2108.07041, 2021 | 11 | 2021 |
| Warp: On the benefits of weight averaged rewarded policies A Ramé, J Ferret, N Vieillard, R Dadashi, L Hussenot, PL Cedoz, ... arXiv preprint arXiv:2406.16768, 2024 | 9 | 2024 |
| Kl-entropy-regularized rl with a generative model is minimax optimal T Kozuno, W Yang, N Vieillard, T Kitamura, Y Tang, J Mei, P Ménard, ... arXiv preprint arXiv:2205.14211, 2022 | 9 | 2022 |
| Training reinforcement learning agents using augmented temporal difference learning MF Geist, N Vieillard, OC Pietquin US Patent App. 17/347,264, 2021 | 4 | 2021 |
| Regularization and variance-weighted regression achieves minimax optimality in linear MDPs: theory and practice T Kitamura, T Kozuno, Y Tang, N Vieillard, M Valko, W Yang, J Mei, ... International Conference on Machine Learning, 17135-17175, 2023 | 2 | 2023 |
| Imitating language via scalable inverse reinforcement learning M Wulfmeier, M Bloesch, N Vieillard, A Ahuja, J Bornschein, S Huang, ... The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | 1 | 2024 |
Matthieu GeistCohere (ex Google, on leave of Professor, Université de Lorraine)E-mailová adresa ověřena na: univ-lorraine.fr