Researchers have analyzed how gradient heterogeneity affects the optimization of Transformer models. They found that when gradients vary significantly across different parts of a model, it hinders the convergence of standard gradient descent methods like SGD. In contrast, adaptive optimizers such as Adam and SignSGD are less affected by this issue because they update parameters in a way that is less sensitive to individual gradient norms. The study also shows that layer normalization plays a significant role in creating gradient heterogeneity, particularly in models with post-layer normalization architecture. --- Why it matters: This research matters to AI engineers because it provides insights into the optimization of Transformer models, which are widely used in natural language processing and other applications. Understanding how to mitigate the effects of gradient heterogeneity can lead to more efficient and effective training of these models. Source: https://arxiv.org/abs/2502.00213