Distinguish pretraining, supervised fine-tuning, and preference optimization.

Pretraining learns broad next-token structure from large unlabeled corpora; SFT imitates curated demonstrations; preference optimization shifts outputs toward ranked human or synthetic preferences. Each stage changes a different part of capability and behavior.

Use equations or tensor shapes where they clarify the claim, then name an experiment or ablation that would distinguish competing explanations.