Building adaptive knowledge bases for evolving continual learning models

Abstract

Continual learning addresses catastrophic forgetting and knowledge transfer when learning from task streams. Dynamic architectures have introduced task-specific components like adapters layered over fixed pre-trained backbones. However, identifying the task of a new input remains a core challenge, leading to task-agnostic and dynamic detection methods. Existing approaches often overlook the reuse of previously learned adapters, missing opportunities for efficient forward and backwards transfer. We propose Continual Adapter-Based Learning (CABLE), a reinforcement learning framework that computes gradient similarity between new examples and past tasks. This similarity score drives a policy that assigns existing adapters when beneficial, rewarding improved performance and reducing reliance on newly initialised parameters. CABLE adopts a dynamic adapter routing strategy without assuming prior task labels. Evaluations on image classification and time series forecasting show that CABLE mitigates catastrophic forgetting and promotes efficient knowledge transfer across tasks.