| Rt-2: Vision-language-action models transfer web knowledge to robotic control B Zitkovich, T Yu, S Xu, P Xu, T Xiao, F Xia, J Wu, P Wohlhart, S Welker, ... Conference on Robot Learning, 2165-2183, 2023 | 1132* | 2023 |
| Open x-embodiment: Robotic learning datasets and rt-x models JJ Lim IEEE International Conference on Robotics and Automation, 2024 | 589* | 2024 |
| When should we prefer offline reinforcement learning over behavioral cloning? A Kumar, J Hong, A Singh, S Levine International Conference on Learning Representations, 2021 | 161* | 2021 |
| Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning M Nakamoto, S Zhai, A Singh, M Sobol Mark, Y Ma, C Finn, A Kumar, ... Advances in Neural Information Processing Systems 36, 62244-62269, 2023 | 123 | 2023 |
| A workflow for offline model-free robotic reinforcement learning A Kumar, A Singh, S Tian, C Finn, S Levine arXiv preprint arXiv:2109.10813, 2021 | 101 | 2021 |
| Preference fine-tuning of llms should leverage suboptimal, on-policy data F Tajwar, A Singh, A Sharma, R Rafailov, J Schneider, T Xie, S Ermon, ... arXiv preprint arXiv:2404.14367, 2024 | 77 | 2024 |
| Pre-training for robots: Offline rl enables learning new tasks from a handful of trials A Kumar, A Singh, F Ebert, M Nakamoto, Y Yang, C Finn, S Levine arXiv preprint arXiv:2210.05178, 2022 | 76 | 2022 |
| Robotic offline rl from internet videos via value-function pre-training C Bhateja, D Guo, D Ghosh, A Singh, M Tomar, Q Vuong, Y Chebotar, ... arXiv preprint arXiv:2309.13041, 2023 | 22 | 2023 |
| Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though V Xiang, C Snell, K Gandhi, A Albalak, A Singh, C Blagden, D Phung, ... arXiv preprint arXiv:2501.04682, 2025 | 21 | 2025 |
| Offline rl with realistic datasets: Heteroskedasticity and support constraints A Singh, A Kumar, Q Vuong, Y Chebotar, S Levine arXiv preprint arXiv:2211.01052, 2022 | 21* | 2022 |
| Rt-2: Vision-language-action models transfer web knowledge to robotic control, 2023 A Brohan, N Brown, J Carbajal, Y Chebotar, X Chen, K Choromanski, ... URL https://arxiv. org/abs/2307.15818, 0 | 20 | |
| A mobile application for keyword search in real-world scenes S Pundlik, A Singh, G Baghel, V Baliutaviciute, G Luo IEEE Journal of Translational Engineering in Health and Medicine 7, 1-10, 2019 | 12 | 2019 |
| Adaptive inference-time compute: Llms can predict if they can do better, even mid-generation R Manvi, A Singh, S Ermon arXiv preprint arXiv:2410.02725, 2024 | 9 | 2024 |
| D5rl: Diverse datasets for data-driven deep reinforcement learning R Rafailov, K Hatch, A Singh, L Smith, A Kumar, I Kostrikov, ... arXiv preprint arXiv:2408.08441, 2024 | 6 | 2024 |
| Cognitive behaviors that enable self-improving reasoners, or, four habits of highly effective stars K Gandhi, A Chakravarthy, A Singh, N Lile, ND Goodman arXiv preprint arXiv:2503.01307, 2025 | 4 | 2025 |
| Robotic offline rl from internet videos via value-function learning C Bhateja, D Guo, D Ghosh, A Singh, M Tomar, Q Vuong, Y Chebotar, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 16977 …, 2024 | 4 | 2024 |
| Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models A Albalak, D Phung, N Lile, R Rafailov, K Gandhi, L Castricato, A Singh, ... arXiv preprint arXiv:2502.17387, 2025 | 1 | 2025 |
| Personalized Preference Fine-tuning of Diffusion Models M Dang, A Singh, L Zhou, S Ermon, J Song arXiv preprint arXiv:2501.06655, 2025 | 1 | 2025 |
| Test-time alignment via hypothesis reweighting Y Lee, J Williams, H Marklund, A Sharma, E Mitchell, A Singh, C Finn arXiv preprint arXiv:2412.08812, 2024 | 1 | 2024 |
| FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users A Singh, S Hsu, K Hsu, E Mitchell, S Ermon, T Hashimoto, A Sharma, ... arXiv preprint arXiv:2502.19312, 2025 | | 2025 |
Sergey LevineUC Berkeley, Physical IntelligenceVerified email at eecs.berkeley.edu
