4.3k citations · 6.8k across the 7 of their papers we have counts for
8 papers
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang +17
Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic,…
Unsupervised Neural Machine Translation with Generative Language Models Only
Jesse Michael Han, Igor Babuschkin, Harrison Edwards +8
We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot a…
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…
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…
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
Matthias Plappert, Marcin Andrychowicz, Alex Ray +9
The purpose of this technical report is two-fold. First of all, it introduces a suite of challenging continuous control tasks (integrated with OpenAI Gym) based on currently existi…
Domain Randomization and Generative Models for Robotic Grasping
Joshua Tobin, Lukas Biewald, Rocky Duan +8
Deep learning-based robotic grasping has made significant progress thanks to algorithmic improvements and increased data availability. However, state-of-the-art models are often tr…