2 citations · 4 across the 6 of their papers we have counts for
11 papers
Towards Robust Scaling Laws for Optimizers
Alexandra Volkova, Mher Safaryan, Christoph H. Lampert +1
The quality of Large Language Model (LLM) pretraining depends on multiple factors, including the compute budget and the choice of optimization algorithm. Empirical scaling laws are…
MT-DAO: Multi-Timescale Distributed Adaptive Optimizers with Local Updates
Alex Iacob, Andrej Jovanovic, Mher Safaryan +6
Training large models with distributed data parallelism (DDP) requires frequent communication of gradients across workers, which can saturate bandwidth. Infrequent communication st…
Unified Scaling Laws for Compressed Representations
Andrei Panferov, Alexandra Volkova, Ionut-Vlad Modoranu +3
Scaling laws have shaped recent advances in machine learning by enabling predictable scaling of model performance based on model size, computation, and data volume. Concurrently, t…
DES-LOC: Desynced Low Communication Adaptive Optimizers for Training Foundation Models
Alex Iacob, Lorenzo Sani, Mher Safaryan +8
Scaling foundation model training with Distributed Data Parallel (DDP) methods is bandwidth-limited. Existing infrequent communication methods like Local SGD were designed to synch…
FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models
Ionut-Vlad Modoranu, Mher Safaryan, Erik Schultheis +3
Low-rank optimization has emerged as a promising direction in training large language models (LLMs) to improve running time and reduce the memory usage of adaptive optimizers by co…
LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics
Thomas Robert, Mher Safaryan, Ionut-Vlad Modoranu +1
We introduce LDAdam, a memory-efficient optimizer for training large models, that performs adaptive optimization steps within lower dimensional subspaces, while consistently explor…