Schulman, John

Trust region policy optimization

Schulman, J., Levine, S., Moritz, P., Jordan, M. I., & Abbeel, P. (2015) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 4,578

Training Language Models to Follow Instructions with Human Feedback

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · Advances in Neural Information Processing Systems 35

Natural Language Processing TechniquesTopic Modeling

Cited by 3,117

High-Dimensional Continuous Control Using Generalized Advantage Estimation

Schulman, J., Moritz, P., Levine, S., Jordan, M., & Abbeel, P. (2015) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 2,103

On First-Order Meta-Learning Algorithms

Nichol, A., Achiam, J., & Schulman, J. (2018) · arXiv

Domain Adaptation and Few-Shot LearningMachine Learning and Algorithms

Cited by 1,438

Concrete Problems in AI Safety

Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 1,314

Benchmarking Deep Reinforcement Learning for Continuous Control

Duan, Y., Chen, X., Houthooft, R., Schulman, J., & Abbeel, P. (2016) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 1,114

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., & Levine, S. (2018) · Robotics: Science and Systems XIV

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 847

OpenAI Gym

Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 582

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., & Abbeel, P. (2016) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 503

Leveraging Procedural Generation to Benchmark Reinforcement Learning

Cobbe, K., Hesse, C., Hilton, J., & Schulman, J. (2019) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 196

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

Scaling Laws for Autoregressive Generative Modeling

Henighan, T., Kaplan, J., Katz, M., Chen, M., Hesse, C., Jackson, J., Jun, H., Brown, T. B., Dhariwal, P., Gray, S., et al. (2020) · arXiv

Advanced Neural Network ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 150

Learning from Demonstrations Through the Use of Non-rigid Registration

Schulman, J., Ho, J., Lee, C., & Abbeel, P. (2016) · Springer Tracts in Advanced Robotics

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 133

Unsolved Problems in ML Safety

Hendrycks, D., Carlini, N., Schulman, J., & Steinhardt, J. (2021) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 95

Phasic Policy Gradient

Cobbe, K., Hilton, J., Klimov, O., & Schulman, J. (2020) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 83

Scaling up Gaussian Belief Space Planning Through Covariance-Free Trajectory Optimization and Automatic Differentiation

Patil, S., Kahn, G., Laskey, M., Schulman, J., Goldberg, K., & Abbeel, P. (2015) · Springer Tracts in Advanced Robotics

Robotic Path Planning AlgorithmsReinforcement Learning in Robotics

Cited by 72

Reasoning Models Don't Always Say What They Think

Chen, Y., Benton, J., Radhakrishnan, A., Uesato, J., Denison, C., Schulman, J., Somani, A., Hase, P., Wagner, M., Roger, F., et al. (2025) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 18

Measuring short-form factuality in large language models

Wei, J., Karina, N., Chung, H. W., Jiao, Y. J., Papay, S., Glaese, A., Schulman, J., & Fedus, W. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 14

Rule Based Rewards for Language Model Safety

Mu, T., Helyar, A., Heidecke, J., Achiam, J., Vallone, A., Kivlichan, I., Lin, M., Beutel, A., Schulman, J., & Weng, L. (2024) · Advances in Neural Information Processing Systems 37

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 14