arXiv AI
Oct 9

Emergent Inverse-Depth Scaling From Nonlinearity In Attention

The paper investigates how nonlinearity in attention mechanisms affects scaling laws in large language models. It finds that nonlinear attention leads to an inverse-depth decay of loss across various data spectra, enabling models to learn both strong and weak spectral directions simultaneously. This behavior contrasts with linear-attention models, which rely on a power-law data spectrum and learn directions sequentially.

By Zirui Peng, Yizhou Liu, Ziming Liu, Jeff Gore