Stage 2 — Instruction Fine-Tuning: Teaching the Model to Follow Prompts
~15 min read
Instruction fine-tuning (IFT) turns a text-completing pre-trained model into a conversational assistant by training it on instruction-response pairs, teaching it to answer questions, follow formatting, and summarize rather than just continue text.
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Key points
- •IFT trains on instruction-response pairs, not raw text — teaching the model to follow prompts, not just continue them
- •Before IFT, a pre-trained model may just continue a question rather than answering it
- •After IFT, the model can reliably answer questions, summarize, and write code on request
- •IFT's data comes from curated instruction/response examples, which is far more expensive to scale than raw pre-training text
- •Diminishing returns from more instruction data motivate the next stage: preference fine-tuning via reinforcement learning