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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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06/12/2022
Self-critiquing models for assisting human evaluators
We fine-tune large language models to write natural language critiques (...
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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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01/20/2022
Safe Deep RL in 3D Environments using Human Feedback
Agents should avoid unsafe behaviour during both training and deployment...
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09/22/2021
Recursively Summarizing Books with Human Feedback
A major challenge for scaling machine learning is training models to per...
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07/07/2021
Evaluating Large Language Models Trained on Code
We introduce Codex, a GPT language model fine-tuned on publicly availabl...
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05/30/2021
Institutionalising Ethics in AI through Broader Impact Requirements
Turning principles into practice is one of the most pressing challenges ...
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11/13/2020
Active Reinforcement Learning: Observing Rewards at a Cost
Active reinforcement learning (ARL) is a variant on reinforcement learni...
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09/19/2020
Hidden Incentives for Auto-Induced Distributional Shift
Decisions made by machine learning systems have increasing influence on ...
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06/24/2020
Quantifying Differences in Reward Functions
For many tasks, the reward function is too complex to be specified proce...
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04/28/2020
Pitfalls of learning a reward function online
In some agent designs like inverse reinforcement learning an agent needs...
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12/05/2019
Learning Human Objectives by Evaluating Hypothetical Behavior
We seek to align agent behavior with a user's objectives in a reinforcem...
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12/14/2018
Scaling shared model governance via model splitting
Currently the only techniques for sharing governance of a deep learning ...
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11/19/2018
Scalable agent alignment via reward modeling: a research direction
One obstacle to applying reinforcement learning algorithms to real-world...
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11/15/2018
Reward learning from human preferences and demonstrations in Atari
To solve complex real-world problems with reinforcement learning, we can...
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06/05/2018
Learning to Follow Language Instructions with Adversarial Reward Induction
Recent work has shown that deep reinforcement-learning agents can learn ...
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11/27/2017
AI Safety Gridworlds
We present a suite of reinforcement learning environments illustrating v...
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06/12/2017
Deep reinforcement learning from human preferences
For sophisticated reinforcement learning (RL) systems to interact useful...
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05/30/2017
Universal Reinforcement Learning Algorithms: Survey and Experiments
Many state-of-the-art reinforcement learning (RL) algorithms typically a...
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11/28/2016
Nonparametric General Reinforcement Learning
Reinforcement learning (RL) problems are often phrased in terms of Marko...
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09/16/2016
A Formal Solution to the Grain of Truth Problem
A Bayesian agent acting in a multi-agent environment learns to predict t...
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09/16/2016
Exploration Potential
We introduce exploration potential, a quantity that measures how much a ...
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04/12/2016
Loss Bounds and Time Complexity for Speed Priors
This paper establishes for the first time the predictive performance of ...
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02/25/2016
Thompson Sampling is Asymptotically Optimal in General Environments
We discuss a variant of Thompson sampling for nonparametric reinforcemen...
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10/19/2015
On the Computability of AIXI
How could we solve the machine learning and the artificial intelligence ...
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10/16/2015
Bad Universal Priors and Notions of Optimality
A big open question of algorithmic information theory is the choice of t...
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05/18/2015
