Supervised Fine-Tuning (SFT): Static Labels, Matching Completions
~12 min read
SFT fine-tunes a model on a fixed dataset of prompt-completion pairs, adjusting weights so the model's output matches the given completions — the book's baseline fine-tuning approach before RFT.
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Key points
- •SFT's process (per the book): start with a static labeled dataset of prompt-completion pairs, adjust weights to match those completions, deploy the best checkpoint
- •'Static' means the correct answer for every example is fixed before training starts — someone (human or synthetic pipeline) already decided what's correct
- •Teacher-forcing trains the model against the known-correct completion at every step, rather than its own prior (possibly wrong) predictions
- •The book's key framing: SFT uses static data and often memorizes answers, rather than learning to discover new correct reasoning paths
- •SFT excels when 'correct' can be written down in advance as a specific target (support responses, code style, structured extraction)