Concept card

Instruction Tuning (SFT) — From Pre-trained to Helpful

As of 2026-07-24T14:00:00Z

Edited by Cédric-Olivier Mâry, sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

What is Instruction Tuning (SFT) — From Pre-trained to Helpful?

SFT turns a raw pretrained LLM's habit of continuing text into instruction-following by fine-tuning on curated (instruction, response) pairs — but it teaches behavior only, never new knowledge.

Instruction tuning — usually called Supervised Fine-Tuning, or SFT — is the training step that turns a raw, pre-trained language model into something that behaves like an assistant. A pre-trained model has only ever learned to predict the next word in enormous amounts of text; it has no built-in notion that a question should be followed by an answer rather than by more questions. Ask a purely pre-trained model "What is the capital of France?" and it may just as plausibly continue with "What is the capital of Germany?" because, statistically, that is what often follows a question in its training corpus. SFT is what teaches the model that a question deserves an answer, an instruction deserves compliance, and a request deserves a helpful, well-formatted response.

How it works

The mechanism is simple relative to what it produces. A curated dataset of instruction-response pairs — anywhere from ten thousand to a few million examples — is used to continue training the pre-trained model, using the same next-token-prediction loss as pre-training, just on a small, carefully written dataset instead of a scrape of the open internet. The model's weights shift toward producing the demonstrated style of response given a demonstrated style of instruction. Crucially, SFT does not add new world knowledge — that lives in the enormous pre-training corpus. SFT teaches behavior: format compliance, tone, refusal patterns, instruction-following.

Why it matters

Unprompted, a pretrained model answers "What is the capital of France?" with more questions, not an answer — SFT is what fixes that specific failure.
InstructGPT's 1.3B SFT model beat raw 175B GPT-3 in human ratings — proof that alignment work outweighs raw scale.
SFT can only imitate demonstrations, not rank quality — it inherits every confidently-wrong example in its training set, which is exactly the gap RLHF, Constitutional AI, and DPO close.

Key points

SFT teaches behaviour (instruction-following, format, tone, refusals), NOT knowledge — knowledge comes from pre-training.
Dataset shape — 10K to 1M (instruction, response) pairs; quality dominates quantity past ~50K examples (LIMA 2023).
Foundational works — FLAN (Google 2021) multi-task; InstructGPT (OpenAI 2022) free-form human-written; Self-Instruct / Alpaca teacher-bootstrapped.
Key result — 1.3B SFT-tuned model preferred over 175B raw GPT-3 by humans (InstructGPT 2022) — alignment > scale on usefulness.
Limits — no notion of quality ranking, can't learn from negatives, propagates demonstration errors; needs RLHF/DPO/CAI on top.
Instruction Tuning (SFT) — From Pre-trained to Helpful is mastered only when it changes a named buyer decision.
Start with the semantic contract and control model before demonstrating the tool.
Use current SAP, analyst, study, KG, and news signals as evidence, not decoration.
Separate verified facts from directional trends and modeled assumptions.
Define owner, metric, threshold, support path, and rollback before scaling.

Terms used on this page

Supervised Fine-Tuning (SFT): The instruction-tuning phase that uses standard supervised next-token prediction on a curated dataset of (instruction, response) pairs to teach a pre-trained LLM to follow instructions and behave like an assistant; the foundation phase before any preference optimisation.
FLAN: Google's 2021 work (Finetuned Language Net) that demonstrated multi-task instruction tuning over 60+ NLP datasets formatted as instructions produces strong zero-shot generalisation to unseen tasks.
InstructGPT: OpenAI's 2022 paper (Ouyang et al.) that combined SFT on human-written free-form demonstrations with RLHF; the canonical recipe behind ChatGPT and the proof that a 1.3B aligned model beats a 175B raw model on usefulness.
LIMA: Meta 2023 result (Less Is More for Alignment) showing 1,000 carefully curated SFT examples produce a model competitive with 50K+ examples — quality dominates quantity in instruction data.
Decision owner: The accountable person who accepts the trade-off and funds the next action.
Semantic contract: The shared definition of business terms, metrics, entities, and access rules used by tools and teams.
Control plane: The layer that applies policy, access, lineage, monitoring, and escalation across the operating model.
Evidence grade: A label that separates verified fact, directional signal, modeled assumption, and field observation.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks · the facts worth quoting.

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