Portrait of Razvan Pascanu

Razvan Pascanu

Affiliate Member
r.pascanu@gmail.com
Senior Research Scientist, Google DeepMind
Research Topics
Continual Learning
Deep Learning
Deep Neural Networks
Few-Shot Learning
Generalization
Geometric Deep Learning
Graph Neural Networks
Lifelong Learning
Machine Learning Theory
Mechanistic Interpretability
Neural Networks
Optimization
Recurrent Neural Networks
Reinforcement Learning
Representation Learning
Website
Google Scholar
LinkedIn

Publications

Why do LLMs attend to the first token?
Federico Barbero
'Alvaro Arroyo
Xiangming Gu
Christos Perivolaropoulos
Michael M. Bronstein
Petar Velivckovi 'c
Razvan Pascanu
2025-04-03
ArXiv (preprint)
arxiv.org
arxiv.org
Why do LLMs attend to the first token?
Federico Barbero
'Alvaro Arroyo
Xiangming Gu
Christos Perivolaropoulos
Michael M. Bronstein
Petar Velivckovi 'c
Razvan Pascanu
2025-04-03
ArXiv (preprint)
arxiv.org
arxiv.org
NoProp: Training Neural Networks without Back-propagation or Forward-propagation
Qinyu Li
Yee Whye Teh
Razvan Pascanu
2025-03-31
ArXiv (preprint)
arxiv.org
arxiv.org
How do language models learn facts? Dynamics, curricula and hallucinations
Nicolas Zucchet
Jorg Bornschein
Stephanie Chan
Andrew Lampinen
Razvan Pascanu
Soham De
2025-03-27
ArXiv (preprint)
arxiv.org
arxiv.org
From Markov to Laplace: How Mamba In-Context Learns Markov Chains
Marco Bondaschi
Nived Rajaraman
Xiuying Wei
Kannan Ramchandran
Razvan Pascanu
Caglar Gulcehre
Michael C. Gastpar
Ashok Vardhan Makkuva
2025-02-14
ArXiv (preprint)
doi.org
arxiv.org
From Markov to Laplace: How Mamba In-Context Learns Markov Chains
Marco Bondaschi
Nived Rajaraman
Xiuying Wei
Kannan Ramchandran
Razvan Pascanu
Caglar Gulcehre
Michael C. Gastpar
Ashok Vardhan Makkuva
While transformer-based language models have driven the AI revolution thus far, their computational complexity has spurred growing interest … (see more)in viable alternatives, such as structured state space sequence models (SSMs) and Selective SSMs. Among these, Mamba (S6) and its variant Mamba-2 have shown remarkable inference speed ups over transformers while achieving comparable or superior performance on complex language modeling tasks. However, despite these architectural innovations and empirical successes, the fundamental learning capabilities of Mamba remain poorly understood. In this paper, we address this gap by studying in-context learning (ICL) on Markov chains and uncovering a surprising phenomenon: unlike transformers, even a single-layer Mamba efficiently learns the in-context Laplacian smoothing estimator, which is both Bayes and minimax optimal, for all Markovian orders. To explain this, we theoretically characterize the representation capacity of Mamba and reveal the fundamental role of convolution in enabling it to represent the optimal Laplacian smoothing. These theoretical insights align strongly with empirical results and, to the best of our knowledge, represent the first formal connection between Mamba and optimal statistical estimators. Finally, we outline promising research directions inspired by these findings.
2025-02-14
ArXiv (preprint)
arxiv.org
arxiv.org
Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons
Simon Dufort-Labbé
Pierluca D'Oro
Evgenii Nikishin
Razvan Pascanu
Irina Rish
Pierre-Luc Bacon
Aristide Baratin
2025-02-13
TMLR (accepted)
doi.org
openreview.net
Agency Is Frame-Dependent
David Abel
Andre Barreto
Michael Bowling
Will Dabney
Shi Dong
Steven Hansen
Anna Harutyunyan
Khimya Khetarpal
Clare Lyle
Razvan Pascanu
Georgios Piliouras
Doina Precup
Jonathan Richens
Mark Rowland
Tom Schaul
Satinder Singh
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science… (see more), and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.
2025-02-06
ArXiv (preprint)
doi.org
arxiv.org
Agency Is Frame-Dependent
David Abel
Andre Barreto
Michael Bowling
Will Dabney
Shi Dong
Steven Hansen
A. Harutyunyan
Khimya Khetarpal
Clare Lyle
Razvan Pascanu
Georgios Piliouras
Doina Precup
Jonathan Richens
Mark Rowland
Tom Schaul
Satinder Singh
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science… (see more), and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.
2025-02-06
ArXiv (preprint)
arxiv.org
arxiv.org
Torque-Aware Momentum
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Gintare Karolina Dziugaite
Razvan Pascanu
Sarath Chandar
Efficiently exploring complex loss landscapes is key to the performance of deep neural networks. While momentum-based optimizers are widely … (see more)used in state-of-the-art setups, classical momentum can still struggle with large, misaligned gradients, leading to oscillations. To address this, we propose Torque-Aware Momentum (TAM), which introduces a damping factor based on the angle between the new gradients and previous momentum, stabilizing the update direction during training. Empirical results show that TAM, which can be combined with both SGD and Adam, enhances exploration, handles distribution shifts more effectively, and improves generalization performance across various tasks, including image classification and large language model fine-tuning, when compared to classical momentum-based optimizers.
2024-12-25
ArXiv (preprint)
doi.org
arxiv.org
Torque-Aware Momentum
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Gintare Karolina Dziugaite
Razvan Pascanu
Sarath Chandar
2024-12-25
ArXiv (preprint)
doi.org
arxiv.org
TRecViT: A Recurrent Video Transformer
Viorica Puatruaucean
Xu Owen He
Joseph Heyward
Chuhan Zhang
Mehdi S. M. Sajjadi
George-Cristian Muraru
Artem Zholus
Mahdi Karami
Ross Goroshin
Yutian Chen 0001
Simon Kayode Osindero
João Carreira
Razvan Pascanu
We propose a novel block for video modelling. It relies on a time-space-channel factorisation with dedicated blocks for each dimension: gate… (see more)d linear recurrent units (LRUs) perform information mixing over time, self-attention layers perform mixing over space, and MLPs over channels. The resulting architecture TRecViT performs well on sparse and dense tasks, trained in supervised or self-supervised regimes. Notably, our model is causal and outperforms or is on par with a pure attention model ViViT-L on large scale video datasets (SSv2, Kinetics400), while having
2024-12-18
ArXiv (preprint)
doi.org
arxiv.org