Supervised and Instruction Fine-Tuning

Supervised fine-tuning (SFT) means: show the model many examples of “given this input, produce that output,” and update weights so it copies the desired pattern. Instruction fine-tuning (IFT) is the chat-friendly version of that idea — teach the model to follow natural-language instructions.

Intuition

Simple analogy: SFT is like tutoring with answer keys. Each row says what good looks like. After enough examples, the model internalizes the habit — not only for that exact wording, but for similar asks.

How it works

Supervised fine-tuning (SFT)

• You need clear inputs and target outputs.
• Format must be consistent (same schema, same role tags if you use chat).
• Loss usually focuses on predicting the target tokens (the answer), not forcing the model to memorize the user’s question as if it wrote it.

Instruction fine-tuning (IFT)

Instruction data often looks like:

• A user instruction (and maybe context)
• An ideal assistant response

Training teaches the model: when someone asks in natural language, answer helpfully in the expected style.

SFT vs IFT (practical view)

	SFT (broad)	IFT (instruction-focused)
Data	Any labeled input→output pairs	Instruction / chat-style pairs
Goal	Task skill (classify, extract, draft…)	Follow instructions in assistant form
Overlap	IFT is a common modern form of SFT for chat models	Same training idea; different data shape

Many people say “SFT” when they mean instruction tuning a chat model. That is fine — just know which data format you are using.

Effect of instruction fine-tuning

After IFT, models usually become better at:

• Following directions
• Producing useful assistant-style answers
• Sticking to requested formats

They still need good data. Garbage instructions teach garbage habits.

What goes wrong

• Mixing random formats in one JSONL file so the model never sees a stable pattern.
• Training on answers only, with unclear instructions.
• Expecting IFT to install fresh facts that belong in RAG.

One-line summary

Supervised fine-tuning teaches from labeled examples; instruction fine-tuning is that idea applied to chat-style “follow my request” data.

Key terms

• SFT — Supervised fine-tuning on labeled pairs.
• Instruction fine-tuning (IFT) — SFT focused on instruction→response behavior.
• Chat template — The role/format wrapper used at train and serve time.