When to Use SFT vs RFT: The Book's Decision Tree
~12 min read
The book's exact decision flowchart: labeled data or not? Verifiable outcome or not? Large or tiny dataset? Each branch points to a specific, concrete choice — SFT, RFT, or RLHF.
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Key points
- •Root question: do you have labeled (ground-truth) data at all?
- •No labels -> is the task verifiable? Not verifiable -> RLHF (needs human preference signals); verifiable -> RFT (correctness auto-checked)
- •Have labels + large dataset -> SFT (direct imitation is efficient and stable with enough examples)
- •Have labels + tiny dataset -> does reasoning/CoT help the task? Yes -> RFT (explore via reward despite few labels); No -> SFT anyway
- •The tree converts a fuzzy 'which technique sounds better' choice into concrete questions about your own data and task