3 papers
cs.LG2026
Transformers Trained via Gradient Descent Can Provably Learn a Class of Teacher Models
Chenyang Zhang, Qingyue Zhao, Quanquan Gu +1
Transformers have achieved great success across a wide range of applications, yet the theoretical foundations underlying their success remain largely unexplored. To demystify the s…
cs.LG2025
Robust Layerwise Scaling Rules by Proper Weight Decay Tuning
Zhiyuan Fan, Yifeng Liu, Qingyue Zhao +2
Empirical scaling laws prescribe how to allocate parameters, data, and compute, while maximal-update parameterization (P) enables learning-rate transfer across widths by equaliz…
cs.LG2025
MARS-M: When Variance Reduction Meets Matrices
Yifeng Liu, Angela Yuan, Quanquan Gu
Matrix-based preconditioned optimizers, such as Muon, have recently been shown to be more efficient than scalar-based optimizers for training large-scale neural networks, including…