Continual Learning

Continual learning is the challenge of training a model on new data without it forgetting previously learned knowledge. Neural networks exhibit catastrophic forgetting: when trained on a new task or dataset, weight updates to accommodate new information overwrite weights that encoded previous knowledge.

For LLMs, this means you cannot simply keep training a deployed model on new information as the world changes. You must either retrain from scratch (expensive), fine-tune carefully with regularization techniques that limit forgetting, or use retrieval (RAG) to provide current information at inference time.

Active research approaches include: elastic weight consolidation, memory replay (include old data in new training), and modular architectures that add new capacity rather than overwriting old.