Direct preference optimization: Your language model is secretly a reward model R Rafailov, A Sharma, E Mitchell, CD Manning, S Ermon, C Finn
Advances in Neural Information Processing Systems 36, 2024
530 2024 Combo: Conservative offline model-based policy optimization T Yu, A Kumar, R Rafailov, A Rajeswaran, S Levine, C Finn
Advances in neural information processing systems 34, 28954-28967, 2021
324 2021 Offline reinforcement learning from images with latent space models R Rafailov, T Yu, A Rajeswaran, C Finn
Learning for dynamics and control, 1154-1168, 2021
112 2021 Offline meta-reinforcement learning with advantage weighting E Mitchell, R Rafailov, XB Peng, S Levine, C Finn
International Conference on Machine Learning, 7780-7791, 2021
96 2021 Open x-embodiment: Robotic learning datasets and rt-x models A Padalkar, A Pooley, A Jain, A Bewley, A Herzog, A Irpan, A Khazatsky, ...
arXiv preprint arXiv:2310.08864, 2023
82 2023 Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback K Tian, E Mitchell, A Zhou, A Sharma, R Rafailov, H Yao, C Finn, ...
arXiv preprint arXiv:2305.14975, 2023
76 2023 Visual adversarial imitation learning using variational models R Rafailov, T Yu, A Rajeswaran, C Finn
Advances in Neural Information Processing Systems 34, 3016-3028, 2021
36 2021 Vision-based manipulators need to also see from their hands K Hsu, MJ Kim, R Rafailov, J Wu, C Finn
arXiv preprint arXiv:2203.12677, 2022
27 2022 On the sum of powered distances to certain sets of points on the circle N Nikolov, R Rafailov
Pacific journal of mathematics 253 (1), 157-168, 2011
23 2011 On extremums of sums of powered distances to a finite set of points N Nikolov, R Rafailov
Geometriae Dedicata 167 (1), 69-89, 2013
18 2013 Diffusion model alignment using direct preference optimization B Wallace, M Dang, R Rafailov, L Zhou, A Lou, S Purushwalkam, S Ermon, ...
arXiv preprint arXiv:2311.12908, 2023
16 2023 Contrastive prefence learning: Learning from human feedback without rl J Hejna, R Rafailov, H Sikchi, C Finn, S Niekum, WB Knox, D Sadigh
arXiv preprint arXiv:2310.13639, 2023
15 2023 Open x-embodiment: Robotic learning datasets and RT-x models Q Vuong, S Levine, HR Walke, K Pertsch, A Singh, R Doshi, C Xu, J Luo, ...
Towards Generalist Robots: Learning Paradigms for Scalable Skill Acquisition …, 2023
11 2023 An emulator for fine-tuning large language models using small language models E Mitchell, R Rafailov, A Sharma, C Finn, CD Manning
arXiv preprint arXiv:2310.12962, 2023
9 2023 Disentangling length from quality in direct preference optimization R Park, R Rafailov, S Ermon, C Finn
arXiv preprint arXiv:2403.19159, 2024
4 2024 MOTO: Offline pre-training to online fine-tuning for model-based robot learning R Rafailov, KB Hatch, V Kolev, JD Martin, M Phielipp, C Finn
Conference on Robot Learning, 3654-3671, 2023
3 2023 Aligning Modalities in Vision Large Language Models via Preference Fine-tuning Y Zhou, C Cui, R Rafailov, C Finn, H Yao
arXiv preprint arXiv:2402.11411, 2024
2 2024 Offline retraining for online rl: Decoupled policy learning to mitigate exploration bias MS Mark, A Sharma, F Tajwar, R Rafailov, S Levine, C Finn
arXiv preprint arXiv:2310.08558, 2023
2 2023 Example-based offline reinforcement learning without rewards K Hatch, T Yu, R Rafailov, C Finn
Proceedings of Machine Learning Research vol 144, 1-17, 2022
2 2022 From to : Your Language Model is Secretly a Q-Function R Rafailov, J Hejna, R Park, C Finn
arXiv preprint arXiv:2404.12358, 2024
1 2024