| 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, 53728-53741, 2023 | 2803 | 2023 |
| Open x-embodiment: Robotic learning datasets and rt-x models JJ Lim IEEE International Conference on Robotics and Automation, 2024 | 511* | 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 | 472 | 2021 |
| 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 | 266 | 2023 |
| Openvla: An open-source vision-language-action model MJ Kim, K Pertsch, S Karamcheti, T Xiao, A Balakrishna, S Nair, ... arXiv preprint arXiv:2406.09246, 2024 | 255 | 2024 |
| Diffusion model alignment using direct preference optimization B Wallace, M Dang, R Rafailov, L Zhou, A Lou, S Purushwalkam, S Ermon, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 157 | 2024 |
| 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 | 147 | 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 | 127 | 2021 |
| Disentangling length from quality in direct preference optimization R Park, R Rafailov, S Ermon, C Finn arXiv preprint arXiv:2403.19159, 2024 | 92 | 2024 |
| Direct preference optimization: Your language model is secretly a reward model, 2024 R Rafailov, A Sharma, E Mitchell, S Ermon, CD Manning, C Finn URL https://arxiv. org/abs/2305.18290 2305, 2023 | 92 | 2023 |
| From to : Your Language Model is Secretly a Q-Function R Rafailov, J Hejna, R Park, C Finn arXiv preprint arXiv:2404.12358, 2024 | 91 | 2024 |
| 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 | 73 | 2024 |
| 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 | 72 | 2024 |
| Contrastive preference 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 | 54 | 2023 |
| Is model collapse inevitable? breaking the curse of recursion by accumulating real and synthetic data M Gerstgrasser, R Schaeffer, A Dey, R Rafailov, H Sleight, J Hughes, ... arXiv preprint arXiv:2404.01413, 2024 | 52 | 2024 |
| Agent q: Advanced reasoning and learning for autonomous ai agents P Putta, E Mills, N Garg, S Motwani, C Finn, D Garg, R Rafailov arXiv preprint arXiv:2408.07199, 2024 | 48 | 2024 |
| 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 | 48 | 2021 |
| 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 | 43 | 2023 |
| Vision-Based Manipulators Need to Also See from Their Hands. K Hsu, MJ Kim, R Rafailov, J Wu, C Finn ICLR, 2022 | 43 | 2022 |
| Scaling laws for reward model overoptimization in direct alignment algorithms R Rafailov, Y Chittepu, R Park, HS Sikchi, J Hejna, B Knox, C Finn, ... Advances in Neural Information Processing Systems 37, 126207-126242, 2024 | 42 | 2024 |
