1.5k citations · 2.3k across the 6 of their papers we have counts for
11 papers
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun +55
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex p…
A Generalizable Approach to Learning Optimizers
Diogo Almeida, Clemens Winter, Jie Tang +1
A core issue with learning to optimize neural networks has been the lack of generalization to real world problems. To address this, we describe a system designed from a generalizat…
Asymmetric self-play for automatic goal discovery in robotic manipulation
OpenAI OpenAI, Matthias Plappert, Raul Sampedro +13
We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects. We rely on asymmetric self-play…
Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models
Lei M. Zhang, Matthias Plappert, Wojciech Zaremba
We propose a method to predict the sim-to-real transfer performance of RL policies. Our transfer metric simplifies the selection of training setups (such as algorithm, hyperparamet…
Solving Rubik's Cube with a Robot Hand
OpenAI, Ilge Akkaya, Marcin Andrychowicz +16
We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key comp…
Learning Dexterous In-Hand Manipulation
OpenAI, Marcin Andrychowicz, Bowen Baker +14
We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The tra…