• Memory-preserving strategies for lifelong learning cohere around stabilizing older knowledge while absorbing new tasks, via model consolidation, functional regularization, replay-based methods, and knowledge transfer between models. This pattern is evidenced by deep model consolidation, functional regularization in function space, replay/meta-learning approaches and teacher-student transfer across tasks. [1], [2], [5], [17], [14]

• Meta-learning frameworks aim to learn robust forgetting-avoidance strategies from task sequences, enabling rapid adaptation with minimal forgetting through learned update rules, replay policies, and task-aware optimization. Examples include learned continual learning strategies and sparse experience replay. [5], [18], [12], [14], [10]

• Cross-domain knowledge sharing and representation transfer enable scalable lifelong learning, using shared/decomposed representations, cross-domain transfer, and multimodal factorization to reuse knowledge across tasks and domains. [3], [7], [16], [6], [20]

• Generative replay and synthetic data generation underpin continual learning by rehearsing past tasks without storing raw data, including lifelong generative modeling and language-model-based replay. [4], [10]

• Online continual learning emphasizes dynamic architectures, selective capacity growth, and task-aware transformations to maintain scalability while handling long task streams. [8], [11], [17], [16], [9]