424 citations · 1.4k across the 9 of their papers we have counts for
9 papers
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Hunter Lightman, Vineet Kosaraju, Yura Burda +7
In recent years, large language models have greatly improved in their ability to perform complex multi-step reasoning. However, even state-of-the-art models still regularly produce…
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos
Bowen Baker, Ilge Akkaya, Peter Zhokhov +6
Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities. H…
Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft
Ingmar Kanitscheider, Joost Huizinga, David Farhi +9
An important challenge in reinforcement learning is training agents that can solve a wide variety of tasks. If tasks depend on each other (e.g. needing to learn to walk before lear…
Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences
Bowen Baker
Multi-agent reinforcement learning (MARL) has shown recent success in increasingly complex fixed-team zero-sum environments. However, the real world is not zero-sum nor does it hav…
Emergent Tool Use From Multi-Agent Autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov +4
Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocu…
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…