Catastrophic Forgetting: Why Fine-Tuning Can Make Models Dumber
Catastrophic forgetting is the tendency for fine-tuning to sharpen a language model on a narrow task while quietly degrading capabilities it never touched during training. It happens because fine-tuning overwrites shared weights rather than adding isolated new knowledge, and the severity ranges from negligible to severe depending on method, learning rate, and how repetitive the data is. As fine-tuning becomes a default lever for teams shipping specialized AI features, the gap between "it got better at X" and "it got worse at everything else" is turning into a quiet source of production incidents. For PMs, the fix isn't avoiding fine-tuning, it's building an eval suite that checks capabilities you didn't train for, not just the one you did.
