Catastrophic Forgetting

Catastrophic forgetting occurs when a neural network significantly degrades its performance on previously learned tasks after being fine-tuned on new data. In LLM fine-tuning, a model trained too aggressively on a narrow domain can lose general language capabilities and reasoning skills present in the base model. Mitigation strategies include using lower learning rates, mixing original pretraining data into the fine-tuning corpus, and using PEFT methods that preserve most base model weights unchanged.