3.4k citations · 4.1k across the 12 of their papers we have counts for
17 papers
A General Theoretical Paradigm to Understand Learning from Human Preferences
Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot +4
The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preference…
Understanding Self-Predictive Learning for Reinforcement Learning
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond +13
We study the learning dynamics of self-predictive learning for reinforcement learning, a family of algorithms that learn representations by minimizing the prediction error of their…
Shaking the foundations: delusions in sequence models for interaction and control
Pedro A. Ortega, Markus Kunesch, Grégoire Delétang +16
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domai…
Geometric Entropic Exploration
Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade +7
Exploration is essential for solving complex Reinforcement Learning (RL) tasks. Maximum State-Visitation Entropy (MSVE) formulates the exploration problem as a well-defined policy…
BYOL works even without batch statistics
Pierre H. Richemond, Jean-Bastien Grill, Florent Altché +8
Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image representation. From an augmented view of an image, BYOL trains an online network to predict a tar…
Bootstrap your own latent: A new approach to self-supervised Learning
Jean-Bastien Grill, Florian Strub, Florent Altché +11
We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target…