fine tuning - from checpoint -- avoiding catastrophic forgetting of ai n retraining on new data alone

 Incremental training (fine‑tuning)

A few common ways to keep the forgetting in check are:

Regularization – penalize large changes to important weights (e.g., Elastic Weight Consolidation).
Replay / experience replay – mix in a small sample of the original data while fine‑tuning.
Adapters or LoRA – freeze most of the original weights and train only a tiny set of extra parameters, so the core knowledge stays intact.
Checkpoint averaging – keep a copy of the original checkpoint and merge it with the fine‑tuned version.