A Mechanistic Study of Transformers Training Dynamics

Understand

Large-scale pretraining of transformers has been central to the success of foundation models.

• However, the scale of those models limits our understanding of the mechanisms at play during optimization.
• In this work, we study the training dynamics of transformers in a controlled and interpretable setting.
• On the sparse modular addition task, we demonstrate that specialized attention circuits, called clustering heads, can be implemented during gradient descent to solve the problem.