Portrait of Michael Rabbat is unavailable

Michael Rabbat

Associate Industry Member
Associate professor, McGill University, Department of Electrical and Computer Engineering
Research Scientist, Facebook AI Research
Research Topics
Distributed Systems
Optimization
Representation Learning

Biography

Mike Rabbat is an associate industry member of Mila – Quebec Artificial Intelligence Institute and director of research science in the Fundamental AI Research (FAIR) team at Meta.

Rabbat’s research interests include efficient and robust representation learning, in particular self-supervised learning. He is also interested in optimization for efficient model training.

Publications

IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environments
Florian Bordes
Quentin Garrido
Justine T Kao
Adina Williams
Michael Rabbat
Emmanuel Dupoux
We present IntPhys 2, a video benchmark designed to evaluate the intuitive physics understanding of deep learning models. Building on the or… (see more)iginal IntPhys benchmark, IntPhys 2 focuses on four core principles related to macroscopic objects: Permanence, Immutability, Spatio-Temporal Continuity, and Solidity. These conditions are inspired by research into intuitive physical understanding emerging during early childhood. IntPhys 2 offers a comprehensive suite of tests, based on the violation of expectation framework, that challenge models to differentiate between possible and impossible events within controlled and diverse virtual environments. Alongside the benchmark, we provide performance evaluations of several state-of-the-art models. Our findings indicate that while these models demonstrate basic visual understanding, they face significant challenges in grasping intuitive physics across the four principles in complex scenes, with most models performing at chance levels (50%), in stark contrast to human performance, which achieves near-perfect accuracy. This underscores the gap between current models and human-like intuitive physics understanding, highlighting the need for advancements in model architectures and training methodologies.
2025-06-11
ArXiv (preprint)
arxiv.org
arxiv.org
IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environments
Florian Bordes
Quentin Garrido
Justine T Kao
Adina Williams
Michael Rabbat
Emmanuel Dupoux
We present IntPhys 2, a video benchmark designed to evaluate the intuitive physics understanding of deep learning models. Building on the or… (see more)iginal IntPhys benchmark, IntPhys 2 focuses on four core principles related to macroscopic objects: Permanence, Immutability, Spatio-Temporal Continuity, and Solidity. These conditions are inspired by research into intuitive physical understanding emerging during early childhood. IntPhys 2 offers a comprehensive suite of tests, based on the violation of expectation framework, that challenge models to differentiate between possible and impossible events within controlled and diverse virtual environments. Alongside the benchmark, we provide performance evaluations of several state-of-the-art models. Our findings indicate that while these models demonstrate basic visual understanding, they face significant challenges in grasping intuitive physics across the four principles in complex scenes, with most models performing at chance levels (50%), in stark contrast to human performance, which achieves near-perfect accuracy. This underscores the gap between current models and human-like intuitive physics understanding, highlighting the need for advancements in model architectures and training methodologies.
2025-06-11
ArXiv (preprint)
arxiv.org
arxiv.org
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mahmoud Assran
Adrien Bardes
David Fan
Quentin Garrido
Russell Howes
Mojtaba Komeili
Matthew J. Muckley
Ammar Rizvi
Claire Roberts
Koustuv Sinha
Artem Zholus
Sergio Arnaud
Abha Gejji
Ada Martin
Francois Robert Hogan
Daniel Dugas
Piotr Bojanowski
Vasil Khalidov
Patrick Labatut
Francisco Massa … (see 13 more)
Marc Szafraniec
K. Krishnakumar
Yong Li
Xiaodong Ma
Sarath Chandar
Franziska Meier
Yann LeCun
Michael Rabbat
Nicolas Ballas
Fair at Meta
Mila - Québec
AI Institute
Polytechnique Montréal
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supe… (see more)rvised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.
2025-06-11
ArXiv (preprint)
arxiv.org
arxiv.org
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mahmoud Assran
Adrien Bardes
David Fan
Quentin Garrido
Russell Howes
Mojtaba Komeili
Matthew J. Muckley
Ammar Rizvi
Claire Roberts
Koustuv Sinha
Artem Zholus
Sergio Arnaud
Abha Gejji
Ada Martin
Francois Robert Hogan
Daniel Dugas
Piotr Bojanowski
Vasil Khalidov
Patrick Labatut
Francisco Massa … (see 13 more)
Marc Szafraniec
K. Krishnakumar
Yong Li
Xiaodong Ma
Sarath Chandar
Franziska Meier
Yann LeCun
Michael Rabbat
Nicolas Ballas
Fair at Meta
Mila - Québec
AI Institute
Polytechnique Montréal
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supe… (see more)rvised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.
2025-06-11
ArXiv (preprint)
arxiv.org
arxiv.org
Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D
Paul McVay
Sergio Arnaud
Ada Martin
Arjun Majumdar
Krishna Murthy
Phillip Thomas
Ruslan Partsey
Daniel Dugas
Abha Gejji
Alexander Sax
Vincent-Pierre Berges
Mikael Henaff
Ayush Jain
Ang Cao
Ishita Prasad
Mrinal Kalakrishnan
Michael Rabbat
Nicolas Ballas
Mahmoud Assran
Oleksandr Maksymets … (see 2 more)
Aravind Rajeswaran
Franziska Meier
2025-05-01
ICML.cc/2025/Conference (poster)
doi.org
openreview.net
Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D
Sergio Arnaud
Paul McVay
Ada Martin
Arjun Majumdar
Krishna Murthy
Phillip Thomas
Ruslan Partsey
Daniel Dugas
Abha Gejji
Alexander Sax
Vincent-Pierre Berges
Mikael Henaff
Ayush Jain
Ang Cao
Ishita Prasad
Mrinal Kalakrishnan
Michael Rabbat
Nicolas Ballas
Mido Assran
Oleksandr Maksymets … (see 2 more)
Aravind Rajeswaran
Franziska Meier
2025-04-19
ArXiv (preprint)
arxiv.org
arxiv.org
Scaling Language-Free Visual Representation Learning
David Fan
Shengbang Tong
Jiachen Zhu
Koustuv Sinha
Zhuang Liu
Xinlei Chen
Michael Rabbat
Nicolas Ballas
Yann LeCun
Amir Bar
Saining Xie
2025-04-01
ArXiv (preprint)
arxiv.org
arxiv.org
Scaling Language-Free Visual Representation Learning
David Fan
Shengbang Tong
Jiachen Zhu
Koustuv Sinha
Zhuang Liu
Xinlei Chen
Michael Rabbat
Nicolas Ballas
Yann LeCun
Amir Bar
Saining Xie
2025-04-01
arXiv (published)
doi.org
arxiv.org
Intuitive physics understanding emerges from self-supervised pretraining on natural videos
Quentin Garrido
Nicolas Ballas
Mahmoud Assran
Adrien Bardes
Laurent Najman
Michael Rabbat
Emmanuel Dupoux
Yann LeCun
We investigate the emergence of intuitive physics understanding in general-purpose deep neural network models trained to predict masked regi… (see more)ons in natural videos. Leveraging the violation-of-expectation framework, we find that video prediction models trained to predict outcomes in a learned representation space demonstrate an understanding of various intuitive physics properties, such as object permanence and shape consistency. In contrast, video prediction in pixel space and multimodal large language models, which reason through text, achieve performance closer to chance. Our comparisons of these architectures reveal that jointly learning an abstract representation space while predicting missing parts of sensory input, akin to predictive coding, is sufficient to acquire an understanding of intuitive physics, and that even models trained on one week of unique video achieve above chance performance. This challenges the idea that core knowledge -- a set of innate systems to help understand the world -- needs to be hardwired to develop an understanding of intuitive physics.
2025-02-17
ArXiv (preprint)
arxiv.org
arxiv.org
Intuitive physics understanding emerges from self-supervised pretraining on natural videos
Quentin Garrido
Nicolas Ballas
Mahmoud Assran
Adrien Bardes
Laurent Najman
Michael Rabbat
Emmanuel Dupoux
Yann LeCun
We investigate the emergence of intuitive physics understanding in general-purpose deep neural network models trained to predict masked regi… (see more)ons in natural videos. Leveraging the violation-of-expectation framework, we find that video prediction models trained to predict outcomes in a learned representation space demonstrate an understanding of various intuitive physics properties, such as object permanence and shape consistency. In contrast, video prediction in pixel space and multimodal large language models, which reason through text, achieve performance closer to chance. Our comparisons of these architectures reveal that jointly learning an abstract representation space while predicting missing parts of sensory input, akin to predictive coding, is sufficient to acquire an understanding of intuitive physics, and that even models trained on one week of unique video achieve above chance performance. This challenges the idea that core knowledge -- a set of innate systems to help understand the world -- needs to be hardwired to develop an understanding of intuitive physics.
2025-02-17
ArXiv (preprint)
doi.org
arxiv.org
Accelerating neural network training: An analysis of the AlgoPerf competition
Priya Kasimbeg
Frank Schneider
Runa Eschenhagen
Juhan Bae
Chandramouli Shama Sastry
Mark Saroufim
BOYUAN FENG
Less Wright
Edward Z. Yang
Zachary Nado
Sourabh Medapati
Philipp Hennig
Michael Rabbat
George E. Dahl
The goal of the AlgoPerf: Training Algorithms competition is to evaluate practical speed-ups in neural network training achieved solely by i… (see more)mproving the underlying training algorithms. In the external tuning ruleset, submissions must provide workload-agnostic hyperparameter search spaces, while in the self-tuning ruleset they must be completely hyperparameter-free. In both rulesets, submissions are compared on time-to-result across multiple deep learning workloads, training on fixed hardware. This paper presents the inaugural AlgoPerf competition's results, which drew 18 diverse submissions from 10 teams. Our investigation reveals several key findings: (1) The winning submission in the external tuning ruleset, using Distributed Shampoo, demonstrates the effectiveness of non-diagonal preconditioning over popular methods like Adam, even when compared on wall-clock runtime. (2) The winning submission in the self-tuning ruleset, based on the Schedule Free AdamW algorithm, demonstrates a new level of effectiveness for completely hyperparameter-free training algorithms. (3) The top-scoring submissions were surprisingly robust to workload changes. We also discuss the engineering challenges encountered in ensuring a fair comparison between different training algorithms. These results highlight both the significant progress so far, and the considerable room for further improvements.
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Accelerating neural network training: An analysis of the AlgoPerf competition
Priya Kasimbeg
Frank Schneider
Runa Eschenhagen
Juhan Bae
Chandramouli Shama Sastry
Mark Saroufim
BOYUAN FENG
Less Wright
Edward Z. Yang
Zachary Nado
Sourabh Medapati
Philipp Hennig
Michael Rabbat
George E. Dahl
The goal of the AlgoPerf: Training Algorithms competition is to evaluate practical speed-ups in neural network training achieved solely by i… (see more)mproving the underlying training algorithms. In the external tuning ruleset, submissions must provide workload-agnostic hyperparameter search spaces, while in the self-tuning ruleset they must be completely hyperparameter-free. In both rulesets, submissions are compared on time-to-result across multiple deep learning workloads, training on fixed hardware. This paper presents the inaugural AlgoPerf competition's results, which drew 18 diverse submissions from 10 teams. Our investigation reveals several key findings: (1) The winning submission in the external tuning ruleset, using Distributed Shampoo, demonstrates the effectiveness of non-diagonal preconditioning over popular methods like Adam, even when compared on wall-clock runtime. (2) The winning submission in the self-tuning ruleset, based on the Schedule Free AdamW algorithm, demonstrates a new level of effectiveness for completely hyperparameter-free training algorithms. (3) The top-scoring submissions were surprisingly robust to workload changes. We also discuss the engineering challenges encountered in ensuring a fair comparison between different training algorithms. These results highlight both the significant progress so far, and the considerable room for further improvements.
2025-01-22
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net