263 citations · 392 across the 53 of their papers we have counts for
8 papers · 1 filter
Completed Hyperparameter Transfer across Modules, Width, Depth, Batch and Duration
Bruno Mlodozeniec, Pierre Ablin, Louis Béthune +4
Hyperparameter tuning can dramatically impact training stability and final performance of large-scale models. Recent works on neural network parameterisations, such as P, have e…
Learning Unmasking Policies for Diffusion Language Models
Metod Jazbec, Theo X. Olausson, Louis Béthune +6
Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the promise of being more efficient…
Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining
Thiziri Nait Saada, Louis Bethune, Michal Klein +3
Large-scale models are pretrained on massive web-crawled datasets containing documents of mixed quality, making data filtering essential. A popular method is Classifier-based Quali…
The Geometries of Truth Are Orthogonal Across Tasks
Waiss Azizian, Michael Kirchhof, Eugene Ndiaye +4
Large Language Models (LLMs) have demonstrated impressive generalization capabilities across various tasks, but their claim to practical relevance is still mired by concerns on the…
Scaling Laws for Optimal Data Mixtures
Mustafa Shukor, Louis Bethune, Dan Busbridge +4
Large foundation models are typically trained on data from multiple domains, with the data mixture--the proportion of each domain used--playing a critical role in model performance…
Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging
Pierre Ablin, Angelos Katharopoulos, Skyler Seto +1
Machine learning models are routinely trained on a mixture of different data domains. Different domain weights yield very different downstream performances. We propose the Soup-of-…