The Ideal Continual Learner: An Agent That Never Forgets
This work addresses a key challenge in continual learning for AI systems that need to learn sequentially without forgetting, offering theoretical insights but is incremental as it builds on existing methods.
The paper tackles the problem of catastrophic forgetting in continual learning by proposing the Ideal Continual Learner (ICL) framework, which is theoretically guaranteed to avoid forgetting and unifies existing methods while providing generalization bounds.
The goal of continual learning is to find a model that solves multiple learning tasks which are presented sequentially to the learner. A key challenge in this setting is that the learner may forget how to solve a previous task when learning a new task, a phenomenon known as catastrophic forgetting. To address this challenge, many practical methods have been proposed, including memory-based, regularization-based, and expansion-based methods. However, a rigorous theoretical understanding of these methods remains elusive. This paper aims to bridge this gap between theory and practice by proposing a new continual learning framework called Ideal Continual Learner (ICL), which is guaranteed to avoid catastrophic forgetting by construction. We show that ICL unifies multiple well-established continual learning methods and gives new theoretical insights into the strengths and weaknesses of these methods. We also derive generalization bounds for ICL which allow us to theoretically quantify how rehearsal affects generalization. Finally, we connect ICL to several classic subjects and research topics of modern interest, which allows us to make historical remarks and inspire future directions.