Nachum, Ofir

RT-1: Robotics Transformer for Real-World Control at Scale

Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Dabis, J., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Hsu, J., et al. (2022) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 727

Data-efficient hierarchical reinforcement learning

Nachum, O., Gu, S., Lee, H., & Levine, S. (2018) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 446

GPT-4o System Card

OpenAI, :, Hurst, A., Lerer, A., Goucher, A. P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 180

Identifying and Correcting Label Bias in Machine Learning

Jiang, H., & Nachum, O. (2019) · arXiv

Ethics and Social Impacts of AIPrivacy-Preserving Technologies in Data

Cited by 152

Multimodal Web Navigation with Instruction-Finetuned Foundation Models

Furuta, H., Lee, K.-H., Nachum, O., Matsuo, Y., Faust, A., Gu, S. S., & Gur, I. (2023) · arXiv

Web Data Mining and AnalysisInformation Retrieval and Search Behavior

Cited by 115

Foundation Models for Decision Making: Problems, Methods, and Opportunities

Yang, S., Nachum, O., Du, Y., Wei, J., Abbeel, P., & Schuurmans, D. (2023) · arXiv

Multimodal Machine Learning ApplicationsAI-based Problem Solving and Planning

Cited by 60

Multi-Game Decision Transformers

Lee, K.-H., Nachum, O., Yang, M. (., Lee, L., Freeman, D., Guadarrama, S., Fischer, I., Xu, W., Jang, E., Michalewski, H., et al. (2022) · Advances in Neural Information Processing Systems 35

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 54

OpenAI o1 System Card

OpenAI, :, Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 50

Multi-agent manipulation via locomotion using hierarchical sim2real

Nachum, O., Ahn, M., Ponte, H., Gu, S., & Kumar, V. (2019) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 46

Why Language Models Hallucinate

Kalai, A. T., Nachum, O., Vempala, S. S., & Zhang, E. (2025) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 45

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

Gulcehre, C., Wang, Z., Novikov, A., Paine, T. L., Colmenarejo, S. G., Zolna, K., Agarwal, R., Merel, J., Mankowitz, D., Paduraru, C., et al. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 42

OpenAI GPT-5 System Card

Singh, A., Fry, A., Perelman, A., Tart, A., Ganesh, A., El-Kishky, A., McLaughlin, A., Low, A., Ostrow, A., Ananthram, A., et al. (2026) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 26

Supervised Pretraining Can Learn In-Context Reinforcement Learning

Lee, J., Xie, A., Pacchiano, A., Chandak, Y., Finn, C., Nachum, O., & Brunskill, E. (2023) · Advances in Neural Information Processing Systems 36

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 11

Supervised Pretraining Can Learn In-Context Reinforcement Learning

Lee, J. N., Xie, A., Pacchiano, A., Chandak, Y., Finn, C., Nachum, O., & Brunskill, E. (2023) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 7

Dichotomy of Control: Separating What You Can Control from What You Cannot

Yang, M., Schuurmans, D., Abbeel, P., & Nachum, O. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 6