Irina Rish

Biography

Irina Rish is a full professor at the Université de Montréal (UdeM), where she leads the Autonomous AI Lab, and a core academic member of Mila – Quebec Artificial Intelligence Institute.

In addition to holding a Canada Excellence Research Chair (CERC) and a CIFAR Chair, she leads the U.S. Department of Energy’s INCITE project on Scalable Foundation Models on Summit & Frontier supercomputers at the Oak Ridge Leadership Computing Facility. She co-founded and serves as CSO of Nolano.ai.

Rish’s current research interests include neural scaling laws and emergent behaviors (capabilities and alignment) in foundation models, as well as continual learning, out-of-distribution generalization and robustness.

Before joining UdeM in 2019, she was a research scientist at the IBM T.J. Watson Research Center, where she worked on various projects at the intersection of neuroscience and AI, and led the Neuro-AI challenge. She was awarded the IBM Eminence & Excellence Award and IBM Outstanding Innovation Award (2018), IBM Outstanding Technical Achievement Award (2017) and IBM Research Accomplishment Award (2009).

She holds 64 patents and has published 120 research papers, several book chapters, three edited books and a monograph on sparse modeling.

Current Students

George Adamopoulos

Research Intern

Ivan Anokhin

PhD - Université de Montréal

Co-supervisor :

Samira Ebrahimi Kahou

Rifat Arefin

PhD - Université de Montréal

Arjun Ashok

PhD - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

PhD - McGill University

Principal supervisor :

Blake Richards

Mohammad Javad Darvishi Bayazi

Amin Darabi

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Karim Jerbi

Wagner Drew

Master's Research - Concordia University

Principal supervisor :

Mirco Ravanelli

Mojtaba Faramarzi

PhD - Université de Montréal

Parviz Haggi Mani

Independent visiting researcher - -

parviz.haggi@gmail.com

Nadhir Hassen

Collaborating Alumni - Université de Montréal

Master's Research

Collaborating Alumni - Université de Montréal

Principal supervisor :

Ioannis Mitliagkas

Nizar Islah

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

PhD - Université de Montréal

Zafir Khalid

Master's Research - Concordia University

Principal supervisor :

Master's Research - Université de Montréal

Neeraj Kumar

Collaborating Alumni - Université de Montréal

Gwen Legate

PhD - Concordia University

Principal supervisor :

Eugene Belilovsky

David Lemay

Master's Research - Université de Montréal

amin.mansouri@mila.quebec

Jonathan Lim

Collaborating researcher

Master's Research - Université de Montréal

Collaborating researcher

Andrei Mircea

PhD - Université de Montréal

Collaborating researcher - Université de Montréal

Gabriela Moisescu-Pareja

Collaborating researcher - McGill University

Principal supervisor :

Doina Precup

Timothy Nest

PhD - Université de Montréal

Co-supervisor :

Mohammad Pezeshki

Collaborating researcher

Co-supervisor :

PhD - McGill University

Principal supervisor :

Pouya Bashivan

Mahta Ramezanian

Master's Research - Université de Montréal

Co-supervisor :

Guillaume Dumas

Roland Riachi

Collaborating researcher - Université de Montréal

Matthew Riemer

PhD - Université de Montréal

Alexis Roger

PhD - McGill University

Principal supervisor :

Blake Richards

Vaibhav Singh

PhD - Concordia University

Principal supervisor :

Eugene Belilovsky

Gopeshh Subbaraj

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Collaborating Alumni - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

Publications

Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation History

Tommaso Tosato

Saskia Helbling

Yorguin-Jose Mantilla-Ramos

Mahmood Hegazy

Alberto Tosato

D. Lemay

Guillaume Dumas

2025-08-06

ArXiv (preprint)

Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation History

Tommaso Tosato

Saskia Helbling

Yorguin-Jose Mantilla-Ramos

Mahmood Hegazy

Alberto Tosato

D. Lemay

Guillaume Dumas

Large language models require consistent behavioral patterns for safe deployment, yet their personality-like traits remain poorly understood… (see more). We present PERSIST (PERsonality Stability in Synthetic Text), a comprehensive evaluation framework testing 25+ open-source models (1B-671B parameters) across 500,000+ responses. Using traditional (BFI-44, SD3) and novel LLM-adapted personality instruments, we systematically vary question order, paraphrasing, personas, and reasoning modes. Our findings challenge fundamental deployment assumptions: (1) Even 400B+ models exhibit substantial response variability (SD>0.4); (2) Minor prompt reordering alone shifts personality measurements by up to 20%; (3) Interventions expected to stabilize behavior, such as chain-of-thought reasoning, detailed personas instruction, inclusion of conversation history, can paradoxically increase variability; (4) LLM-adapted instruments show equal instability to human-centric versions, confirming architectural rather than translational limitations. This persistent instability across scales and mitigation strategies suggests current LLMs lack the foundations for genuine behavioral consistency. For safety-critical applications requiring predictable behavior, these findings indicate that personality-based alignment strategies may be fundamentally inadequate.

2025-08-06

ArXiv (preprint)

Revisiting Replay and Gradient Alignment for Continual Pre-Training of Large Language Models

Istabrak Abbes

Gopeshh Subbaraj

Matthew D Riemer

Nizar Islah

Benjamin Therien

Tsuguchika Tabaru

Hiroaki Kingetsu

Sarath Chandar

Training large language models (LLMs) typically involves pre-training on massive corpora, only to restart the process entirely when new data… (see more) becomes available. A more efficient and resource-conserving approach would be continual pre-training, where models are updated with new data rather than retraining from scratch. However, the introduction of new data often causes distribution shifts, leading to performance degradation on previously learned tasks. In this paper, we take a deeper look at two popular proposals for addressing this distribution shift within the continual learning literature: experience replay and gradient alignment. We consider continual pre-training of models within the Llama family of architectures at a large scale across languages with 100 billion tokens of training data in each language, finding that both replay and gradient alignment lead to more stable learning without forgetting. This conclusion holds both as we vary the model scale and as we vary the number and diversity of tasks. Moreover, we are the first to demonstrate the effectiveness of gradient alignment techniques in the context of LLM pre-training and propose an efficient implementation of meta-experience replay (MER) that imbues experience replay with the benefits of gradient alignment despite negligible compute and memory overhead. Our scaling analysis across model sizes and replay rates indicates that small rates of replaying old examples are definitely a more valuable use of compute than investing in model size, but that it is more compute efficient to scale the size of the model than invest in high rates of replaying old examples.

2025-08-03

ArXiv (preprint)

Revisiting Replay and Gradient Alignment for Continual Pre-Training of Large Language Models

Istabrak Abbes

Gopeshh Subbaraj

Matthew D Riemer

Nizar Islah

Benjamin Therien

Tsuguchika Tabaru

Hiroaki Kingetsu

Sarath Chandar

2025-08-03

ArXiv (preprint)

GitChameleon: Evaluating AI Code Generation Against Python Library Version Incompatibilities

Victor May

Justine Gehring

Antonio Orvieto

Muawiz Chaudhary

Samira Ebrahimi Kahou

Massimo Caccia

The rapid evolution of software libraries poses a considerable hurdle for code generation, necessitating continuous adaptation to frequent v… (see more)ersion updates while preserving backward compatibility. While existing code evolution benchmarks provide valuable insights, they typically lack execution-based evaluation for generating code compliant with specific library versions. To address this, we introduce GitChameleon, a novel, meticulously curated dataset comprising 328 Python code completion problems, each conditioned on specific library versions and accompanied by executable unit tests. GitChameleon rigorously evaluates the capacity of contemporary large language models (LLMs), LLM-powered agents, code assistants, and RAG systems to perform version-conditioned code generation that demonstrates functional accuracy through execution. Our extensive evaluations indicate that state-of-the-art systems encounter significant challenges with this task; enterprise models achieving baseline success rates in the 48-51\% range, underscoring the intricacy of the problem. By offering an execution-based benchmark emphasizing the dynamic nature of code libraries, GitChameleon enables a clearer understanding of this challenge and helps guide the development of more adaptable and dependable AI code generation methods. We make the dataset and evaluation code publicly available at https://github.com/mrcabbage972/GitChameleonBenchmark.

2025-07-16

ArXiv (preprint)

GitChameleon: Evaluating AI Code Generation Against Python Library Version Incompatibilities

Victor May

Justine Gehring

Antonio Orvieto

Muawiz Chaudhary

Samira Ebrahimi Kahou

Massimo Caccia

2025-07-16

ArXiv (preprint)

Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training

The ever-growing availability of unlabeled data presents both opportunities and challenges for training artificial intelligence systems. Whi… (see more)le self-supervised learning (SSL) has emerged as a powerful paradigm for extracting meaningful representations from vast amounts of unlabeled data, existing methods still struggle to adapt to the non-stationary, non-IID nature of real-world data streams without forgetting previously learned knowledge. Recent works have adopted a repeated cosine annealing schedule for large-scale continual pre-training; however, these schedules (1) inherently cause forgetting during the re-warming phase and (2) have not been systematically compared to existing continual SSL methods. In this work, we systematically compare the widely used cosine schedule with the recently proposed infinite learning rate schedule and empirically find the latter to be a more effective alternative. Our extensive empirical evaluation across diverse image and language datasets demonstrates that the infinite learning rate schedule consistently enhances continual pre-training performance compared to a repeated cosine decay without being restricted to a fixed iteration budget. For instance, in a small-scale MAE pre-training setup, it outperforms several strong baselines from the literature. We then scale up our experiments to larger MAE pre-training and autoregressive language model pre-training. Our results show that the infinite learning rate schedule remains effective at scale, surpassing repeated cosine decay for both MAE pre-training and zero-shot LM benchmarks.

2025-06-11

ICML.cc/2025/Workshop/ES-FoMo-III (published)

doi.org

openreview.net

MuLoCo: Muon is a practical inner optimizer for DiLoCo

2025-06-11

ICML.cc/2025/Workshop/ES-FoMo-III (published)

doi.org

openreview.net

AIF-GEN: Open-Source Platform and Synthetic Dataset Suite for Reinforcement Learning on Large Language Models

Jacob Chmura

Shahrad Mohammadzadeh

Taz Scott-Talib

Nishanth Anand

2025-06-09

ICML.cc/2025/Workshop/CODEML (published)

openreview.net

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning

Andrei Mircea

Supriyo Chakraborty

Nima Chitsazan

Ekaterina Lobacheva

This work aims to understand how scaling improves language models, specifically in terms of training dynamics. We find that language models … (see more)undergo loss deceleration early in training; an abrupt slowdown in the rate of loss improvement, resulting in piecewise linear behaviour of the loss curve in log-log space. Scaling up the model mitigates this transition by (1) decreasing the loss at which deceleration occurs, and (2) improving the log-log rate of loss improvement after deceleration. We attribute loss deceleration to a type of degenerate training dynamics we term zero-sum learning (ZSL). In ZSL, per-example gradients become systematically opposed, leading to destructive interference in per-example changes in loss. As a result, improving loss on one subset of examples degrades it on another, bottlenecking overall progress. Loss deceleration and ZSL provide new insights into the training dynamics underlying language model scaling laws, and could potentially be targeted directly to improve language models independent of scale. We make our code and artefacts available at: https://github.com/mirandrom/zsl

2025-06-05

ArXiv (preprint)

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field

Vanessa Hadid

Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive proces… (see more)ses with an unparalleled combination of high temporal and spatial precision. MEG data analytics has always relied on advanced signal processing and mathematical and statistical tools for various tasks ranging from data cleaning to probing the signals' rich dynamics and estimating the neural sources underlying the surface-level recordings. Like in most domains, the surge in Artificial Intelligence (AI) has led to the increased use of Machine Learning (ML) methods for MEG data classification. More recently, an emerging trend in this field is using Artificial Neural Networks (ANNs) to address many MEG-related tasks. This review provides a comprehensive overview of how ANNs are being used with MEG data from three vantage points: First, we review work that employs ANNs for MEG signal classification, i.e., for brain decoding. Second, we report on work that has used ANNs as putative models of information processing in the human brain. Finally, we examine studies that use ANNs as techniques to tackle methodological questions in MEG, including artifact correction and source estimation. Furthermore, we assess the current strengths and limitations of using ANNs with MEG and discuss future challenges and opportunities in this field. Finally, by establishing a detailed portrait of the field and providing practical recommendations for the future, this review seeks to provide a helpful reference for both seasoned MEG researchers and newcomers to the field who are interested in using ANNs to enhance the exploration of the complex dynamics of the human brain with MEG.

2025-05-27

Journal of Neural Engineering (published)

doi.org