Foundation Models

Supervised Fine-Tuning (SFT)

Supervised fine-tuning (SFT) is the adaptation of a pretrained model by continued training on curated input-output pairs with a standard supervised loss, specializing broad pretrained capabilities to a target behavior. In robot foundation models, training a vision-language-action model on teleoperated demonstrations is effectively SFT of a vision-language backbone with action outputs. It typically precedes further post-training stages such as reinforcement learning or preference optimization.

Why it matters for physical AI

The pretrain-then-SFT recipe imported from language modeling is now the dominant way robot policies inherit internet-scale visual and semantic knowledge before learning to act.