PolarGrad: A Class of Matrix-Gradient Optimizers from a Unifying Preconditioning Perspective

TL;DR

This paper introduces PolarGrad, a new class of matrix-gradient optimizers based on polar decomposition, providing a unifying framework that explains and improves upon existing optimizers like Adam and Muon in deep learning.

Contribution

It offers a unifying analysis framework for matrix-aware preconditioned methods and introduces PolarGrad, a novel optimizer leveraging polar decomposition for better convergence.

Findings

PolarGrad outperforms Adam and Muon in language model training.

The framework clarifies the effectiveness of structure-aware preconditioners.

PolarGrad demonstrates improved convergence on diverse matrix optimization tasks.

Abstract

The ever-growing scale of deep learning models and training data underscores the critical importance of efficient optimization methods. While preconditioned gradient methods such as Adam and AdamW are the de facto optimizers for training neural networks and large language models, structure-aware preconditioned optimizers like Shampoo and Muon, which utilize the matrix structure of gradients, have demonstrated promising evidence of faster convergence. In this paper, we introduce a unifying framework for analyzing "matrix-aware" preconditioned methods, which not only sheds light on the effectiveness of Muon and related optimizers but also leads to a class of new structure-aware preconditioned methods. A key contribution of this framework is its precise distinction between preconditioning strategies that treat neural network weights as vectors (addressing curvature anisotropy) versus those that consider their matrix structure (addressing gradient anisotropy). This perspective provides new insights into several empirical phenomena in language model pre-training, including Adam's training instabilities, Muon's accelerated convergence, and the necessity of learning rate warmup for Adam. Building upon this framework, we introduce PolarGrad, a new class of preconditioned optimization methods based on the polar decomposition of matrix-valued gradients. As a special instance, PolarGrad includes Muon with updates scaled by the nuclear norm of the gradients. We provide numerical implementations of these methods, leveraging efficient numerical polar decomposition algorithms for enhanced convergence. Our extensive evaluations across diverse matrix optimization problems and language model pre-training tasks demonstrate that PolarGrad outperforms both Adam and Muon.