Unraveling the Gradient Descent Dynamics of Transformers
–Neural Information Processing Systems
While the Transformer architecture has achieved remarkable success across various domains, a thorough theoretical foundation explaining its optimization dynamics is yet to be fully developed. In this study, we aim to bridge this understanding gap by answering the following two core questions: (1) Which types of Transformer architectures allow Gradient Descent (GD) to achieve guaranteed convergence?
Neural Information Processing Systems
Dec-26-2025, 21:03:51 GMT