| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 457 | 2023 |
| Google research football: A novel reinforcement learning environment K Kurach, A Raichuk, P Stańczyk, M Zając, O Bachem, L Espeholt, ... Proceedings of the AAAI conference on artificial intelligence 34 (04), 4501-4510, 2020 | 333 | 2020 |
| Acme: A research framework for distributed reinforcement learning MW Hoffman, B Shahriari, J Aslanides, G Barth-Maron, N Momchev, ... arXiv preprint arXiv:2006.00979, 2020 | 231 | 2020 |
| What matters in on-policy reinforcement learning? a large-scale empirical study M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ... arXiv preprint arXiv:2006.05990, 2020 | 193 | 2020 |
| What matters for on-policy deep actor-critic methods? a large-scale study M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ... International conference on learning representations, 2020 | 151 | 2020 |
| Seed rl: Scalable and efficient deep-rl with accelerated central inference L Espeholt, R Marinier, P Stanczyk, K Wang, M Michalski arXiv preprint arXiv:1910.06591, 2019 | 134 | 2019 |
| Gkd: Generalized knowledge distillation for auto-regressive sequence models R Agarwal, N Vieillard, P Stanczyk, S Ramos, M Geist, O Bachem arXiv preprint arXiv:2306.13649, 2023 | 29 | 2023 |
| What matters in on-policy reinforcement learning M Andrychowicz, A Raichuk, P Stanczyk, M Orsini, S Girgin, R Marinier, ... A large-scale empirical study. CoRR, abs/2006.05990 3, 2020 | 29 | 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 | 27 | 2023 |
| Launchpad: A programming model for distributed machine learning research F Yang, G Barth-Maron, P Stańczyk, M Hoffman, S Liu, M Kroiss, A Pope, ... arXiv preprint arXiv:2106.04516, 2021 | 21 | 2021 |
| Perfect Matching for Biconnected Cubic Graphs in O(n log2 n) Time K Diks, P Stanczyk SOFSEM 2010: Theory and Practice of Computer Science: 36th Conference on …, 2010 | 14 | 2010 |
| Rlds: an ecosystem to generate, share and use datasets in reinforcement learning S Ramos, S Girgin, L Hussenot, D Vincent, H Yakubovich, D Toyama, ... arXiv preprint arXiv:2111.02767, 2021 | 12 | 2021 |
| Generalized knowledge distillation for auto-regressive language models R Agarwal, N Vieillard, Y Zhou, P Stanczyk, S Ramos, M Geist, O Bachem arXiv preprint arXiv:2306.13649, 2023 | 5 | 2023 |
| 400 Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, et al. Google research 401 football: A novel reinforcement learning environment K Kurach, A Raichuk, P Stanczyk, M Zajac, O Bachem, L Espeholt arXiv preprint arXiv:1907.11180 402 (10), 2019 | 5 | 2019 |
| Google research football K Kurach, A Raichuk, P Stanczyk, M Zajac, O Bachem, L Espeholt, ... A” Novel Reinforcement Learning Environment”, CoRR, 2019 | 5 | 2019 |
| 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 | 3 | 2024 |
| What matters in on-policy reinforcement learning? a large-scale empirical study (2020) M Andrychowicz, A Raichuk, P Stanczyk, M Orsini, S Girgin, R Marinier, ... arXiv preprint arXiv:2006.05990, 2006 | 3 | 2006 |
| SIO .NET Plug&Play Contest System M Michalski, M Kosieradzki, W Rygielski, P Stańczyk, K Ciebiera, K Diks Perspectives on Computer Science Competitions for (High School) Students, 2005 | 3 | 2005 |
| Reinforcement learning with centralized inference and training L Espeholt, K Wang, MM Michalski, PM Stanczyk, R Marinier US Patent App. 17/764,066, 2022 | | 2022 |
| RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning H Yakubovich, D Toyama, A Gergely, P Stanczyk | | |
