EXPLAINER

Instruction Tuning

techniquefreshness: evolvingsources not yet verified

Instruction tuning fine-tunes a pre-trained language model on many instruction-and-response examples so it follows requests instead of merely continuing text.

Short answer: Instruction tuning is supervised fine-tuning on instruction→response pairs. It turns a base model into an assistant-style model; preference optimisation then refines tone, helpfulness and safety.

What is it?

A raw pre-trained model just continues text. Instruction tuning teaches it, with lots of examples, to treat your text as a request and respond helpfully. It is a main step between a base model and a chat assistant.

Also known as: SFT, supervised fine-tuning, instruct model, instruction following training, FLAN, base model vs instruct model, chat tuning, assistant tuning.

Technical definition

Supervised fine-tuning (SFT) on diverse task instructions (FLAN, Wei et al., 2021; T0; InstructGPT SFT stage, Ouyang et al., 2022), including synthetic instruction data (Self-Instruct, Wang et al., 2022). The standard pipeline is pre-training → instruction tuning → preference optimisation (RLHF (Reinforcement Learning from Human Feedback), Preference Optimization (Aligning Models from Human or AI Preferences)). Data quality and diversity matter more than raw volume; narrow tuning can cause loss of general abilities.

Entity type: technique · Freshness class: evolving.

When should I use it?

Relevant if you adapt open-weight base models; most users consume already-tuned models.

Implementation patterns

  • Curate a small, high-quality, diverse instruction set.
  • Use the model's chat template consistently.
  • Mask the prompt tokens in the loss where appropriate.
  • Hold out an evaluation set of unseen instructions.

Technologies and frameworks

Commonly associated: TRL SFTTrainer, Axolotl, LLaMA-Factory, Hugging Face datasets. Names are examples of what exists, not recommendations, and projects change quickly.

Practical guide

  1. Pick a base model and check its licence.
  2. Collect/curate examples.
  3. Apply its chat template.
  4. Fine-tune (LoRA first).
  5. Evaluate on held-out instructions and regressions (How to Evaluate AI Systems and Outputs).

Common failure modes

  • Template mismatch between training and inference.
  • Low-quality or contaminated data.
  • Over-tuning leading to forgetting or style collapse.
  • Training on outputs whose licence forbids it.

Troubleshooting

  • Model rambles or ignores stop tokens — Check the chat template and end-of-sequence handling.
  • General skills degrade — Lower learning rate/epochs; mix in general data; use LoRA.

Contrasted with

Questions this page answers

  • What is instruction tuning?
  • What is the difference between a base model and an instruct model?
  • What is SFT?
  • How are chat models trained?

Typical goals: understand how base models become assistants; decide whether to use a base or instruct model.

Sources and verification

Status: not independently verified. This entry was authored on 2026-10-06 from general technical knowledge, without live checking of the sources below. Treat every source as a lead to confirm (identifier, title, authorship, current version) before publication or citation.

Freshness class: evolving — Established techniques whose best practice shifts as research and tooling move. Re-review every 6 months.

Canonical sources (unverified leads):

  • Wei et al., Finetuned Language Models Are Zero-Shot Learners (FLAN) (2021) — arXiv:2109.01652
  • Ouyang et al., Training language models to follow instructions with human feedback (2022) — arXiv:2203.02155
  • Wang et al., Self-Instruct (2022) — arXiv:2212.10560
Learning path: Alignment: from RLHF to DPO (step 1 of 10)
Reinforcement Learning →