Zeta: Dual Whitening for Matrix Optimization via Coordinate-Adaptive Preconditioning
Large-scale neural network training increasingly relies on matrix-aware optimizers that exploit the structure of weight parameters beyond element-wise adaptation. However, existing matrix-aware methods such as Muon have an underappreciated vulnerability: their core operation, Newton-Schulz iteration, depends critically on input conditioning, yet the raw momentum matrices exhibit severe coordinate-wise scale heterogeneity. In this paper, we first verify this scale heterogeneity through a chi-square uniformity test, showing that intra-matrix scale imbalance is prevalent across Transformer layers and that coordinate whitening effectively corrects it. Motivated by this finding, we propose Zet...
| Authors | Kaiwen Chen, Shuhai Zhang, Zimo Liu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 June 2026 |
| Last updated at source | 16 June 2026 |
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