12 papers
DASH: Faster Shampoo via Batched Block Preconditioning and Efficient Inverse-Root Solvers
Ionut-Vlad Modoranu, Philip Zmushko, Erik Schultheis +2
Shampoo is one of the leading approximate second-order optimizers: a variant of it has won the MLCommons AlgoPerf competition, and it has been shown to produce models with lower ac…
CAGE: Curvature-Aware Gradient Estimation For Accurate Quantization-Aware Training
Soroush Tabesh, Mher Safaryan, Andrei Panferov +2
Despite significant work on low-bit quantization-aware training (QAT), there is still an accuracy gap between such techniques and native training. To address this, we introduce CAG…
LoRDO: Distributed Low-Rank Optimization with Infrequent Communication
Andrej JovanoviÄ, Alex Iacob, Mher Safaryan +6
Distributed training of foundation models via is limited by interconnect bandwidth. While infrequent communication strategies reduce synchronization frequency, they…
Stochastic Sign Descent Methods: New Algorithms and Better Theory
Mher Safaryan, Peter Richtárik
Various gradient compression schemes have been proposed to mitigate the communication cost in distributed training of large scale machine learning models. Sign-based methods, such…
MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning
Ionut-Vlad Modoranu, Mher Safaryan, Dan Alistarh
With the rise in scale for deep learning models to billions of parameters, the computational cost of fine-tuning remains a significant barrier to deployment. While Low-Rank Adaptat…
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…