Definition
Plain language
Training a model by showing it examples of correct answers and having it imitate them.
As stated in the literature
Supervised Fine-Tuning, a post-training stage in which a model is trained on labeled demonstrations via standard next-token prediction.
Also called: supervised fine-tuning
Why it matters: It's the simplest and cheapest way to inject new behavior into a base model, and it's almost always the first step of any post-training pipeline.
For example, to teach a model to refuse harmful requests politely, you fine-tune it on thousands of (harmful prompt, polite refusal) pairs.
Heard on the show
“A separately-trained dedicated judge — the kind of thing you'd build with supervised fine-tuning or reinforcement learning on actual rollout-quality labels — is the obvious next step.”Episode 003 — How to Pick the Best of Sixteen Coding Agent Rollouts