Achiam, Joshua

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

Constrained Policy Optimization

Achiam, J., Held, D., Tamar, A., & Abbeel, P. (2017) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 804

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

Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning

Achiam, J., & Sastry, S. (2017) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 139

A Hazard Analysis Framework for Code Synthesis Large Language Models

Khlaaf, H., Mishkin, P., Achiam, J., Krueger, G., & Brundage, M. (2022) · arXiv

Software Engineering ResearchEthics and Social Impacts of AI

Cited by 23

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