Rafailov, Rafael

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., & Finn, C. (2023) · Advances in Neural Information Processing Systems 36

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 686

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., & Finn, C. (2023) · arXiv

Ethics and Social Impacts of AIEmbodied and Extended Cognition

Cited by 293

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration 0

O’Neill, A., Rehman, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 210

Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback

Tian, K., Mitchell, E., Zhou, A., Sharma, A., Rafailov, R., Yao, H., Finn, C., & Manning, C. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 123

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Embodiment Collaboration, O'Neill, A., Rehman, A., Gupta, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., et al. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 111

OpenVLA: An Open-Source Vision-Language-Action Model

Kim, M. J., Pertsch, K., Karamcheti, S., Xiao, T., Balakrishna, A., Nair, S., Rafailov, R., Foster, E., Lam, G., Sanketi, P., et al. (2024) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 62

Disentangling Length from Quality in Direct Preference Optimization

Park, R., Rafailov, R., Ermon, S., & Finn, C. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 22

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

Gerstgrasser, M., Schaeffer, R., Dey, A., Rafailov, R., Sleight, H., Hughes, J., Korbak, T., Agrawal, R., Pai, D., Gromov, A., et al. (2024) · arXiv

Generative Adversarial Networks and Image SynthesisTopic Modeling

Cited by 15

Visual adversarial imitation learning using variational models

Rafailov, R., Yu, T., Rajeswaran, A., & Finn, C. (2021) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 12

Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents

Putta, P., Mills, E., Garg, N., Motwani, S., Finn, C., Garg, D., & Rafailov, R. (2024) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 10

Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback

Tian, K., Mitchell, E., Zhou, A., Sharma, A., Rafailov, R., Yao, H., Finn, C., & Manning, C. D. (2023) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 5

Collapse or thrive? perils and promises of synthetic data in a self-generating world

Kazdan, J., Schaeffer, R., Dey, A., Gerstgrasser, M., Rafailov, R., Donoho, D. L., & Koyejo, S. (2024) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 4