4 papers · 1 filter
ORCA: The Annealed Spectral Conditioning Optimizer for Faster, Better LLM Training
Yuanshi Liu, Boyuan Jiang, Liang Hou +4
Modern LLM optimizers such as Muon often produce weight matrices with higher effective rank than Adam, yet further spectral control has delivered only modest gains. We identify a t…
AdaPM: a Partial Momentum Algorithm for LLM Training
Yimu Zhang, Yuanshi Liu, Cong Fang
In the training of large language models, momentum is widely used and often demonstrated to achieve significant acceleration. However, storing momentum typically presents memory ch…
The Optimality of (Accelerated) SGD for High-Dimensional Quadratic Optimization
Haihan Zhang, Yuanshi Liu, Qianwen Chen +1
Stochastic gradient descent (SGD) is a widely used algorithm in machine learning, particularly for neural network training. Recent studies on SGD for canonical quadratic optimizati…
Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization
Yuanshi Liu, Hanzhen Zhao, Yang Xu +2
Gradient-based minimax optimal algorithms have greatly promoted the development of continuous optimization and machine learning. One seminal work due to Yurii Nesterov [Nes83a] est…