7 papers · 1 filter
MuLoCo: Muon is a practical inner optimizer for DiLoCo
Benjamin Thérien, Xiaolong Huang, Aaron Defazio +2
DiLoCo is a powerful framework for training large language models (LLMs), enabling larger optimal batch sizes and increased accelerator utilization under networking constraints. Ho…
LO: Compute-Efficient Meta-Generalization of Learned Optimizers
Benjamin Thérien, Charles-Ãtienne Joseph, Boris Knyazev +3
Learned optimizers (LOs) have the potential to significantly reduce the wall-clock training time of neural networks. However, they can struggle to optimize unseen tasks (meta-gener…
Continual Pre-training of MoEs: How robust is your router?
Benjamin Thérien, Charles-Ãtienne Joseph, Zain Sarwar +7
Sparsely-activated Mixture of Experts (MoE) transformers are promising architectures for foundation models. Compared to dense transformers that require the same amount of floating-…
Overcoming the Communication-Performance Tradeoff in LLM Pretraining
Amir Sarfi, Benjamin Thérien, Joel Lidin +1
Communication-efficient distributed training algorithms (e.g., DiLoCo) have received considerable interest due to their benefits for training large language models (LLMs) in bandwi…
Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training
Vaibhav Singh, Paul Janson, Paria Mehrbod +4
The ever-growing availability of unlabeled data presents both opportunities and challenges for training artificial intelligence systems. While self-supervised learning (SSL) has em…
Meta-learning Optimizers for Communication-Efficient Learning
Charles-Ãtienne Joseph, Benjamin Thérien, Abhinav Moudgil +2
Communication-efficient variants of SGD, specifically local SGD, have received a great deal of interest in recent years. These approaches compute multiple gradient steps locally on…