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05/31/2023
Let's Verify Step by Step
In recent years, large language models have greatly improved in their ab...
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01/31/2023
Scaling laws for single-agent reinforcement learning
Recent work has shown that, in generative modeling, cross-entropy loss i...
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10/19/2022
Scaling Laws for Reward Model Overoptimization
In reinforcement learning from human feedback, it is common to optimize ...
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07/28/2022
Efficient Training of Language Models to Fill in the Middle
We show that autoregressive language models can learn to infill text aft...
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03/04/2022
Training language models to follow instructions with human feedback
Making language models bigger does not inherently make them better at fo...
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12/17/2021
WebGPT: Browser-assisted question-answering with human feedback
We fine-tune GPT-3 to answer long-form questions using a text-based web-...
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10/27/2021
Training Verifiers to Solve Math Word Problems
State-of-the-art language models can match human performance on many tas...
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10/01/2021
Batch size-invariance for policy optimization
We say an algorithm is batch size-invariant if changes to the batch size...
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09/28/2021
Unsolved Problems in ML Safety
Machine learning (ML) systems are rapidly increasing in size, are acquir...
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03/29/2021
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
The NeurIPS 2020 Procgen Competition was designed as a centralized bench...
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01/26/2021
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
Although deep reinforcement learning has led to breakthroughs in many di...
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10/28/2020
Scaling Laws for Autoregressive Generative Modeling
We identify empirical scaling laws for the cross-entropy loss in four do...
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09/09/2020
Phasic Policy Gradient
We introduce Phasic Policy Gradient (PPG), a reinforcement learning fram...
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12/03/2019
Leveraging Procedural Generation to Benchmark Reinforcement Learning
In this report, we introduce Procgen Benchmark, a suite of 16 procedural...
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04/07/2019
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees
Monte Carlo Tree Search (MCTS) algorithms perform simulation-based searc...
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02/06/2019
Semi-Supervised Learning by Label Gradient Alignment
We present label gradient alignment, a novel algorithm for semi-supervis...
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12/06/2018
Quantifying Generalization in Reinforcement Learning
In this paper, we investigate the problem of overfitting in deep reinfor...
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09/14/2018
Model-Based Reinforcement Learning via Meta-Policy Optimization
Model-based reinforcement learning approaches carry the promise of being...
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04/10/2018
Gotta Learn Fast: A New Benchmark for Generalization in RL
In this report, we present a new reinforcement learning (RL) benchmark b...
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03/08/2018
Reptile: a Scalable Metalearning Algorithm
This paper considers metalearning problems, where there is a distributio...
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09/28/2017
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Dexterous multi-fingered hands are extremely versatile and provide a gen...
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07/01/2017
Teacher-Student Curriculum Learning
We propose Teacher-Student Curriculum Learning (TSCL), a framework for a...
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06/05/2017
UCB Exploration via Q-Ensembles
We show how an ensemble of Q^*-functions can be leveraged for more effec...
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11/15/2016
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Count-based exploration algorithms are known to perform near-optimally w...
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11/09/2016
RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning
Deep reinforcement learning (deep RL) has been successful in learning so...
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11/08/2016
Variational Lossy Autoencoder
Representation learning seeks to expose certain aspects of observed data...
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06/21/2016
Concrete Problems in AI Safety
Rapid progress in machine learning and artificial intelligence (AI) has ...
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06/12/2016
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
This paper describes InfoGAN, an information-theoretic extension to the ...
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06/05/2016
OpenAI Gym
OpenAI Gym is a toolkit for reinforcement learning research. It includes...
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05/31/2016
VIME: Variational Information Maximizing Exploration
Scalable and effective exploration remains a key challenge in reinforcem...
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05/09/2016
