Continual Learning Frameworks for Long-Lived Generative AI Systems

Abstract

Generative AI systems deployed in production environments must evolve continuously as world knowledge changes, user needs shift, and deployment domains expand -- yet the dominant paradigm of train-once-deploy-forever treats trained models as static artefacts that cannot incorporate new information without full retraining. Continual learning for generative AI addresses this limitation by enabling models to accumulate new knowledge and capabilities over time while retaining previously learned information -- a challenge compounded for large language models by the catastrophic forgetting problem, the scale of model parameters, and the diversity of knowledge encoded in pre-trained representations. This paper proposes the Generative Continual Learning (GCL) framework, a structured methodology for designing and evaluating continual learning systems for long-lived LLMs, comprising four strategies: experience replay with importance-weighted memory, gradient episodic memory with task-boundary detection, modular architecture expansion with knowledge distillation, and retrieval-augmented knowledge integration. The GCL framework is evaluated through a longitudinal simulation study spanning 24 sequential knowledge update tasks across four knowledge domains -- factual world events, domain knowledge evolution, user preference adaptation, and capability expansion -- on LLaMA-2-7B as the base model. GCL strategies are benchmarked against naive sequential fine-tuning and full retraining baselines on five evaluation criteria: knowledge retention, knowledge integration, backward transfer, forward transfer, and compute efficiency. Results demonstrate that the modular expansion strategy achieves the best retention-integration balance (retention = 94.2%, integration = 87.6%) while retrieval- augmented integration achieves the highest compute efficiency. Gradient episodic memory with task-boundary detection most effectively prevents catastrophic forgetting (mean backward transfer = -0.8%). The study contributes the GCL framework specification, a Continual Learning Evaluation Protocol (CLEP) for generative AI, and empirical evidence on the long-run knowledge evolution dynamics of LLMs.