368 citations · 628 across the 11 of their papers we have counts for
7 papers · 1 filter
Competitive Programming with Large Reasoning Models
OpenAI, :, Ahmed El-Kishky +23
We show that reinforcement learning applied to large language models (LLMs) significantly boosts performance on complex coding and reasoning tasks. Additionally, we compare two gen…
Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer
Greg Yang, Edward J. Hu, Igor Babuschkin +7
Hyperparameter (HP) tuning in deep learning is an expensive process, prohibitively so for neural networks (NNs) with billions of parameters. We show that, in the recently discovere…
Dota 2 with Large Scale Deep Reinforcement Learning
OpenAI, :, Christopher Berner +24
On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as lo…
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…
Parameter Space Noise for Exploration
Matthias Plappert, Rein Houthooft, Prafulla Dhariwal +6
Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent'…
UCB Exploration via Q-Ensembles
Richard Y. Chen, Szymon Sidor, Pieter Abbeel +1
We show how an ensemble of -functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit s…