51 citations · 51 across the 7 of their papers we have counts for
4 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…
Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training
Jakub Krajewski, Amitis Shidani, Dan Busbridge +2
While scaling laws for Large Language Models (LLMs) traditionally focus on proxy metrics like pretraining loss, predicting downstream task performance has been considered unreliabl…
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…
Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection
Louis Bethune, David Grangier, Dan Busbridge +3
A widespread strategy to obtain a language model that performs well on a target domain is to finetune a pretrained model to perform unsupervised next-token prediction on data from…