Concept
Supervised fine-tuning
Version 1 — First definition, written for the AfterQuery Terminal-Bench analysis (frontier-blogs v1).
Supervised fine-tuning is continued training of a pretrained model on curated demonstrations — input paired with the output you want — so that the demonstrated behaviour becomes the model’s default. It is imitation, not trial and error: the model never acts and never gets a reward; it learns what doing the task well looks like from examples of it done well, which makes the curation of those examples the whole game.
In the standard post-training recipe SFT comes first, and the ordering is load-bearing rather than conventional: reinforcement learning needs occasional successes to amplify, and a base model too weak to ever succeed gives it nothing. AfterQuery’s Terminal-Bench write-up states the dependency exactly — the RL stage “only works because the SFT checkpoint is already capable enough to sometimes solve the training tasks,” or as they compress it, “SFT makes meaningful RL possible” — and attributes the larger share of their gain (3.1% → 13.5%, before RL’s 13.5% → 17.0%) to this stage, “which is entirely a function of data quality.” That attribution is also the claim a data-curation company most benefits from; the mechanism is standard, the proportion is theirs.