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09/05/2023
Explaining grokking through circuit efficiency
One of the most surprising puzzles in neural network generalisation is g...
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07/18/2023
Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla
Circuit analysis is a promising technique for understanding the internal...
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03/23/2023
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
To facilitate research in the direction of fine-tuning foundation models...
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01/02/2023
SIRL: Similarity-based Implicit Representation Learning
When robots learn reward functions using high capacity models that take ...
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10/04/2022
Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals
The field of AI alignment is concerned with AI systems that pursue unint...
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05/16/2022
An Empirical Investigation of Representation Learning for Imitation
Imitation learning often needs a large demonstration set in order to han...
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04/14/2022
Retrospective on the 2021 BASALT Competition on Learning from Human Feedback
We held the first-ever MineRL Benchmark for Agents that Solve Almost-Lif...
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07/05/2021
The MineRL BASALT Competition on Learning from Human Feedback
The last decade has seen a significant increase of interest in deep lear...
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04/08/2021
Learning What To Do by Simulating the Past
Since reward functions are hard to specify, recent work has focused on l...
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03/22/2021
Combining Reward Information from Multiple Sources
Given two sources of evidence about a latent variable, one can combine t...
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01/19/2021
Choice Set Misspecification in Reward Inference
Specifying reward functions for robots that operate in environments with...
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01/14/2021
Evaluating the Robustness of Collaborative Agents
In order for agents trained by deep reinforcement learning to work along...
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11/01/2020
The MAGICAL Benchmark for Robust Imitation
Imitation Learning (IL) algorithms are typically evaluated in the same e...
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10/13/2019
On the Utility of Learning about Humans for Human-AI Coordination
While we would like agents that can coordinate with humans, current algo...
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06/23/2019
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
Our goal is for agents to optimize the right reward function, despite ho...
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02/12/2019
Preferences Implicit in the State of the World
Reinforcement learning (RL) agents optimize only the features specified ...
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09/09/2018
