Catastrophic Forgetting

In depth

The tendency of a neural network to lose previously learned skills when it is trained on a new task, because new gradient updates overwrite the same weights the old skills depended on. It is a practical risk in fine-tuning: a model tuned hard on one narrow dataset can get worse at general instruction following or coding. Common mitigations include mixing original data back in, lower learning rates, parameter-efficient methods like LoRA, and Elastic Weight Consolidation, which slows learning on the weights most important to earlier tasks.

Primary source: arxiv.org