What is model warm-starting in continuous learning systems?

Warm-starting initializes a model using previously trained weights instead of training from scratch.

In continuous learning systems, warm-starting reduces training time and improves convergence stability by reusing previous model parameters. It is especially useful in streaming data environments where distributions evolve gradually. However, it risks catastrophic forgetting if not combined with regularization or replay buffers.