Understanding Transformer Optimization via Gradient Heterogeneity
New paper reveals Adam's secret: it's a 'soft' version of SignSGD that handles gradient chaos.
Deep Dive
Researchers Akiyoshi Tomihari and Issei Sato published 'Understanding Transformer Optimization via Gradient Heterogeneity.' Their analysis shows Transformers suffer from wildly varying gradient sizes across layers, which cripples standard SGD. Adam's coordinate-wise normalization acts like SignSGD, making it robust to this 'gradient heterogeneity.' The work identifies Post-LayerNorm architecture as a key culprit. This provides a theoretical foundation for why Adam is essential for training modern LLMs and vision transformers.
Why It Matters
Explains a core engineering choice in AI, potentially leading to more stable and efficient model training.