Schulman, John

Training language models to follow instructions with human feedback

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 4,208

WebGPT: Browser-assisted question-answering with human feedback

Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., et al. (2021) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 272

Let's Verify Step by Step

Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., & Cobbe, K. (2023) · arXiv

Topic ModelingMachine Learning and Algorithms

Cited by 65

Scaling Laws for Reward Model Overoptimization

Gao, L., Schulman, J., & Hilton, J. (2022) · arXiv

Reinforcement Learning in RoboticsEthics and Social Impacts of AI

Cited by 62

Efficient Training of Language Models to Fill in the Middle

Bavarian, M., Jun, H., Tezak, N., Schulman, J., McLeavey, C., Tworek, J., & Chen, M. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 47