# Instruction Tuning

> Instruction tuning is fine-tuning a pretrained language model on many tasks phrased as natural-language instructions, so that it follows unseen instructions zero-shot.

- Identifier: PTL-0010
- Category: Foundations
- Canonical URL: https://protologue.com/t/instruction-tuning/
- Also known as: instruction fine-tuning, supervised fine-tuning, SFT
- Introduced: 2021

## Description

The FLAN work showed that tuning on dozens of instruction-formatted datasets substantially improved zero-shot performance on held-out tasks. Combined with reinforcement learning from human feedback, it produced the instruction-following chat models that most prompting techniques now target.

## Related terms

- [Zero-shot Prompting](https://protologue.com/t/zero-shot-prompting/)
- [Reinforcement Learning from Human Feedback](https://protologue.com/t/rlhf/)

## Sources

- Wei et al. (2021). Finetuned Language Models Are Zero-Shot Learners. https://arxiv.org/abs/2109.01652
- Ouyang et al. (2022). Training language models to follow instructions with human feedback. https://arxiv.org/abs/2203.02155

## Cite this entry

Protologue. (2026). Instruction Tuning. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0010). https://protologue.com/t/instruction-tuning/

License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)