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

On the generalization of language models from in-context learning and finetuning: a controlled study
Andrew Lampinen
Arslan Chaudhry
Stephanie C.Y. Chan
Cody Wild
Diane Wan
Alexander Y. Ku
Alex Ku
Jorg Bornschein
Razvan Pascanu
Murray P. Shanahan
James L McClelland
2025-05-01
arXiv (published)
doi.org
arxiv.org
On the generalization of language models from in-context learning and finetuning: a controlled study
Andrew Lampinen
Arslan Chaudhry
Stephanie C.Y. Chan
Cody Wild
Diane Wan
Alexander Y. Ku
Jorg Bornschein
Razvan Pascanu
Murray P. Shanahan
James L McClelland
Large language models exhibit exciting capabilities, yet can show surprisingly narrow generalization from finetuning. E.g. they can fail to … (see more)generalize to simple reversals of relations they are trained on, or fail to make simple logical deductions based on trained information. These failures to generalize from fine-tuning can hinder practical application of these models. On the other hand, language models' in-context learning shows different inductive biases, and can generalize better in some cases. Here, we explore these differences in generalization between in-context- and fine-tuning-based learning. To do so, we constructed several novel datasets to evaluate and improve models' abilities to generalize from finetuning data. The datasets are designed to create clean tests of generalization, by isolating the knowledge in the dataset from that in pretraining. We expose pretrained large models to controlled subsets of the information in these datasets -- either in context, or through fine-tuning -- and evaluate their performance on test sets that require various types of generalization. We find overall that in data-matched settings, in-context learning can generalize more flexibly than fine-tuning (though we also find some qualifications of prior findings, such as cases when fine-tuning can generalize to reversals embedded in a larger structure of knowledge). We build on these findings to propose a method to enable improved generalization from fine-tuning: adding in-context inferences to finetuning data. We show that this method improves generalization across various splits of our datasets and other benchmarks. Our results have implications for understanding the inductive biases of different modes of learning in language models, and practically improving their performance.
2025-05-01
ArXiv (preprint)
doi.org
arxiv.org
LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities
Thomas Schmied
Jorg Bornschein
Jordi Grau-Moya
Markus Wulfmeier
Razvan Pascanu
2025-04-22
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
LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities
Thomas Schmied
Jorg Bornschein
Jordi Grau-Moya
Markus Wulfmeier
Razvan Pascanu
2025-04-01
arXiv (published)
doi.org
arxiv.org
Why do LLMs attend to the first token?
Federico Barbero
'Alvaro Arroyo
Xiangming Gu
Christos Perivolaropoulos
Michael M. Bronstein
Petar Veličković
Razvan Pascanu
2025-04-01
arXiv (published)
doi.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)
doi.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
NoProp: Training Neural Networks without Full Back-propagation or Full Forward-propagation
Qinyu Li
Yee Whye Teh
Razvan Pascanu
The canonical deep learning approach for learning requires computing a gradient term at each block by back-propagating the error signal from… (see more) the output towards each learnable parameter. Given the stacked structure of neural networks, where each block builds on the representation of the block below, this approach leads to hierarchical representations. More abstract features live on the top blocks of the model, while features on lower blocks are expected to be less abstract. In contrast to this, we introduce a new learning method named NoProp, which does not rely on either forward or backwards propagation across the entire network. Instead, NoProp takes inspiration from diffusion and flow matching methods, where each block independently learns to denoise a noisy target using only local targets and back-propagation within the block. We believe this work takes a first step towards introducing a new family of learning methods that does not learn hierarchical representations -- at least not in the usual sense. NoProp needs to fix the representation at each block beforehand to a noised version of the target, learning a local denoising process that can then be exploited at inference. We demonstrate the effectiveness of our method on MNIST, CIFAR-10, and CIFAR-100 image classification benchmarks. Our results show that NoProp is a viable learning algorithm, is easy to use and computationally efficient. By departing from the traditional learning paradigm which requires back-propagating a global error signal, NoProp alters how credit assignment is done within the network, enabling more efficient distributed learning as well as potentially impacting other characteristics of the learning process.
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
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)
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