Understanding Transformer Optimization via Gradient Heterogeneity
#Transformer #SGD #Adam #Gradient heterogeneity #Optimization #Adaptive optimization #Deep learning #Neural networks #Training dynamics
📌 Key Takeaways
- Introduces gradient heterogeneity as a metric for comparing gradient behavior across transformer parameter blocks.
- Analyzes the optimization challenges specific to transformers when using SGD.
- Explores the empirical advantage of Adam over SGD and seeks underlying causes.
- Highlights the importance of understanding optimizer performance to improve transformer training.
- Provides insights that may guide future optimizer design for large‑scale transformer models.
📖 Full Retelling
This paper, titled *Understanding Transformer Optimization via Gradient Heterogeneity*, was uploaded to arXiv in February 2025. It examines why transformer models are difficult to optimize with stochastic gradient descent (SGD) and therefore heavily rely on adaptive optimizers such as Adam. The authors introduce gradient heterogeneity—the variation in gradient norms across parameter blocks—as a lens for analyzing the optimization process, and investigate why Adam consistently outperforms SGD in practice.
🏷️ Themes
Transformers, Optimization, Gradient heterogeneity, Adaptive optimizers, Stochastic gradient descent, Deep learning performance, Neural network training dynamics
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Original Source
arXiv:2502.00213v4 Announce Type: replace-cross
Abstract: Transformers are difficult to optimize with stochastic gradient descent (SGD) and largely rely on adaptive optimizers such as Adam. Despite their empirical success, the reasons behind Adam's superior performance over SGD remain poorly understood. In this study, we analyze the optimization of Transformer models through the lens of \emph{gradient heterogeneity}, defined as the variation in gradient norms across parameter blocks. We provide
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