Does Fine-Tuning Teach LLMs Facts or Behavior? Exploring How Dataset Size & Parameters Affect Learning

I'm experimenting with fine-tuning small language models and I'm curious about what exactly they learn.

• Do LLMs learn facts (like trivia or static knowledge)?
• Or do they learn behaviors (like formatting, tone, or response patterns)?

I also want to understand:

• How can we tell what the model actually learned during fine-tuning?
• What happens if we change the dataset size or hyperparameters for each type of learning?
• Any tips on isolating behaviors from factual knowledge?

Would love to hear insights, especially if you've done LLM fine-tuning before.