How do frontier LLM systems approach continual learning without full retraining?

Continual learning systems enable LLMs to adapt to new information incrementally while minimizing catastrophic forgetting and retraining costs.

Traditional LLM training pipelines are static:

However, real-world knowledge evolves continuously:

Continual learning aims to keep models updated without retraining entire frontier-scale systems.

Techniques include:

Updating small adapter layers instead of full weights.

Injecting external knowledge dynamically.

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