Foundation Models Meet Continual Learning: Recent Advances, Challenges, and Future Directions

Abstract

Foundation models (FMs) have emerged as powerful pre-trained systems capable of adapting to diverse downstream tasks, while continual learning (CL) aims to enable models to sequentially acquire new knowledge without catastrophically forgetting previous information. This paper examines the synergies between recent advances in FMs and CL techniques. We review key FM capabilities relevant to CL, analyze how FM architectures and training paradigms can enhance CL methods, and explore integrated approaches combining FM and CL principles. Our analysis suggests that FMs' robust representations, transfer abilities, and adaptable architectures offer promising avenues for advancing CL, while CL techniques can enable FMs to continually expand their capabilities in dynamic environments.