Easy Monotonic Policy Iteration. - Joshua Achiam (CoRR, 2016)

Constrained Policy Optimization. - Joshua Achiam; David Held; Aviv Tamar; Pieter Abbeel (ICML, 2017)

Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning. - Joshua Achiam; Shankar Sastry 001 (CoRR, 2017)

On First-Order Meta-Learning Algorithms. - Alex Nichol; Joshua Achiam; John Schulman (CoRR, 2018)

Variational Option Discovery Algorithms. - Joshua Achiam; Harrison Edwards; Dario Amodei; Pieter Abbeel (CoRR, 2018)

Towards Characterizing Divergence in Deep Q-Learning. - Joshua Achiam; Ethan Knight; Pieter Abbeel (CoRR, 2019)

Responsive Safety in Reinforcement Learning by PID Lagrangian Methods. - Adam Stooke; Joshua Achiam; Pieter Abbeel (ICML, 2020)

Evaluating Large Language Models Trained on Code. - Mark Chen 003; Jerry Tworek; Heewoo Jun; Qiming Yuan; Henrique Pondé de Oliveira Pinto; Jared Kaplan; Harri Edwards; Yuri Burda; Nicholas Joseph; Greg Brockman; Alex Ray; Raul Puri; et al. (CoRR, 2021)

A Hazard Analysis Framework for Code Synthesis Large Language Models. - Heidy Khlaaf; Pamela Mishkin; Joshua Achiam; Gretchen Krueger; Miles Brundage (CoRR, 2022)

Rule Based Rewards for Language Model Safety. - Tong Mu; Alec Helyar; Johannes Heidecke; Joshua Achiam; Andrea Vallone; Ian Kivlichan; Molly Lin; Alex Beutel; John Schulman; Lilian Weng (NeurIPS, 2024)