Comparison
Instruction Tuning vs Supervised Fine-Tuning
Instruction Tuning and Supervised Fine-Tuning are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Instruction Tuning
Instruction Tuning comes up when the question is fundamentally about training.
FLAN tuning Google's T5 to follow instructions.
When you would reach for Supervised Fine-Tuning
Supervised Fine-Tuning comes up when the question is fundamentally about training.
Training Llama-3-Base on Anthropic's HH-RLHF "chosen" responses as a first pass.
Frequently asked
What is the difference between Instruction Tuning and Supervised Fine-Tuning?
Instruction Tuning: Instruction tuning is fine-tuning on examples of (instruction, desired response) pairs so a base model learns to follow natural-language directions. Supervised Fine-Tuning: SFT is fine-tuning where each training example has an explicit input and a desired output, supervised by a loss that penalizes deviation from that output.
When should I use Instruction Tuning vs Supervised Fine-Tuning?
Instruction Tuning is the right concept when you are focused on training. Supervised Fine-Tuning applies when you are focused on training.
Are Instruction Tuning and Supervised Fine-Tuning the same thing?
No. Instruction Tuning is training; Supervised Fine-Tuning is training. They are related but address different parts of the AI stack.