Tracking the Feature Dynamics in LLM Training: A Mechanistic Study

Understanding training dynamics and feature evolution is crucial for the mechanistic interpretability of large language models (LLMs). Although sparse autoencoders (SAEs) have been used to identify features within LLMs, a clear picture of how these features evolve during training remains elusive. In this study, we: (1) introduce SAE-Track, a method to efficiently obtain a continual series of SAEs; (2) formulate the process of feature formation and conduct a mechanistic analysis; and (3) analyze and visualize feature drift during training. Our work provides new insights into the dynamics of features in LLMs, enhancing our understanding of training mechanisms and feature evolution.