He is a founding researcher at a Stealth AI startup, tenured researcher at Inria, and the lecturer at the MVA master of ENS Paris-Saclay. He is primarily interested in designing algorithms that would require as little human supervision as possible. He is working on methods and settings that are able to deal with minimal feedback, such as deep reinforcement learning, bandit algorithms, self-supervised learning, or self play. He has recently worked on representation learning, word models and deep (reinforcement) learning algorithms that have some theoretical underpinning. In the past he worked on sequential algorithms with structured decisions where exploiting the structure leads to provably faster learning. He is now working on large large models (LMMs), in particular providing algorithmic solutions for their scalable fine-tuning and alignment. He received his Ph.D. in 2011 from the University of Pittsburgh, before getting a tenure at Inria in 2012 and co-creating Google DeepMind Paris with Rémi Munos. In 2024, he became the principal Llama engineer at Meta, building online reinforcement learning stack and research for Llama 3.