Portrait of Benjamin Therien

Benjamin Therien

PhD - Université de Montréal
Supervisor
Irina Rish
Co-supervisor
Eugene Belilovsky
Research Topics
Deep Learning
Large Language Models (LLM)
Meta-Learning
Optimization
Website
Google Scholar
GitHub

Publications

$\mu$LO: Compute-Efficient Meta-Generalization of Learned Optimizers
Benjamin Therien
Charles-Etienne Joseph
Boris Knyazev
Edouard Oyallon
Irina Rish
Eugene Belilovsky
Learned optimizers (LOs) have the potential to significantly reduce the wall-clock training time of neural networks. However, they can strug… (see more)gle to optimize unseen tasks (*meta-generalize*), especially when training networks wider than those seen during meta-training. To address this, we derive the Maximal Update Parametrization (
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
Continual Pre-training of MoEs: How robust is your router?
Benjamin Therien
Charles-Etienne Joseph
Zain Sarwar
Ashwinee Panda
Anirban Das
Shi-Xiong Zhang
Stephen Rawls
Sambit Sahu
Eugene Belilovsky
Irina Rish
2025-09-25
TMLR (accepted)
doi.org
openreview.net
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
A. Chandar
Irina Rish
2025-09-21
NeurIPS.cc/2025/Workshop/WiML (published)
doi.org
openreview.net
Communication Efficient LLM Pre-training with SparseLoCo
Amir M. Sarfi
Benjamin Therien
Joel Lidin
Eugene Belilovsky
2025-08-20
ArXiv (preprint)
doi.org
arxiv.org
Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training
Vaibhav Singh
Paul Janson
Paria Mehrbod
Adam Ibrahim
Irina Rish
Eugene Belilovsky
Benjamin Therien
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.
2025-06-10
ICML.cc/2025/Workshop/ES-FoMo-III (published)
doi.org
openreview.net
MuLoCo: Muon is a practical inner optimizer for DiLoCo
Benjamin Therien
Xiaolong Huang
Irina Rish
Eugene Belilovsky
2025-06-10
ICML.cc/2025/Workshop/ES-FoMo-III (published)
doi.org
openreview.net
PyLO: Towards Accessible Learned Optimizers in PyTorch
Paul Janson
Benjamin Therien
Quentin Gregory Anthony
Xiaolong Huang
Abhinav Moudgil
Eugene Belilovsky
Learned optimizers have been an active research topic over the past decade, with increasing progress toward practical, general-purpose optim… (see more)izers that can serve as drop-in replacements for widely used methods like Adam. However, recent advances -- such as VeLO, which was meta-trained for 4000 TPU-months -- remain largely inaccessible to the broader community, in part due to their reliance on JAX and the absence of user-friendly packages for applying the optimizers after meta-training. To address this gap, we introduce PyLO, a PyTorch-based library that brings learned optimizers to the broader machine learning community through familiar, widely adopted workflows. Unlike prior work focused on synthetic or convex tasks, our emphasis is on applying learned optimization to real-world large-scale pre-training tasks. Our release includes a CUDA-accelerated version of the small_fc_lopt learned optimizer architecture from (Metz et al., 2022a), delivering substantial speedups -- from 39.36 to 205.59 samples/sec throughput for training ViT B/16 with batch size 32. PyLO also allows us to easily combine learned optimizers with existing optimization tools such as learning rate schedules and weight decay. When doing so, we find that learned optimizers can substantially benefit. Our code is available at https://github.com/Belilovsky-Lab/pylo
2025-06-08
ICML.cc/2025/Workshop/CODEML (published)
doi.org
openreview.net
Can We Learn Communication-Efficient Optimizers?
Charles-Etienne Joseph
Benjamin Therien
Abhinav Moudgil
Boris Knyazev
Eugene Belilovsky
2024-12-31
Trans. Mach. Learn. Res. (published)
doi.org
openreview.net
μLO: Compute-Efficient Meta-Generalization of Learned Optimizers
Benjamin Therien
Charles-Etienne Joseph
Boris Knyazev
Edouard Oyallon
Irina Rish
Eugene Belilovsky
2024-10-09
NeurIPS.cc/2024/Workshop/OPT (published)
doi.org
openreview.net
Simple and Scalable Strategies to Continually Pre-train Large Language Models
Adam Ibrahim
Benjamin Therien
Kshitij Gupta
Mats Leon Richter
Quentin Gregory Anthony
Timothee LESORT
Eugene Belilovsky
Irina Rish
2024-07-07
TMLR (accepted)
doi.org
openreview.net
Learning Optimizers for Local SGD
Charles-Etienne Joseph
Benjamin Therien
Abhinav Moudgil
Boris Knyazev
Eugene Belilovsky
2023-10-26
NeurIPS.cc/2023/Workshop/Federated_Learning (poster)
openreview.net
openreview.net
Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis
Pedro Vianna
Sara‐Ivana Calce
Pamela Boustros
Cassandra Larocque-Rigney
Laurent Patry-Beaudoin
Yi Hui Luo
Emre Aslan
John Marinos
Talal Alamri
Kim‐Nhien Vu
Jessica Murphy-Lavallée
Jean-Sébastien Billiard
Emmanuel Montagnon
Hongliang Li
Samuel Kadoury
Bich Nguyen
Shanel Gauthier
Benjamin Therien
Irina Rish
Eugene Belilovsky … (see 4 more)
Guy Wolf
Michael Chassé
Guy Cloutier
An Tang
Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose T… (see more)o evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of κ statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue.
2023-09-30
Radiology (unknown)
doi.org