Instruction Tuning
Also called instruction fine-tuning, supervised fine-tuning, SFT.
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.
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.
Sources
- Wei et al. (2021). Finetuned Language Models Are Zero-Shot Learners.
- Ouyang et al. (2022). Training language models to follow instructions with human feedback.
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/
BibTeX
@misc{protologue_instruction_tuning,
title = {Instruction Tuning},
author = {{Protologue}},
year = {2026},
howpublished = {Protologue: A Taxonomy of Prompting and LLM Techniques, v1.0.0},
note = {Entry PTL-0010},
url = {https://protologue.com/t/instruction-tuning/}
}