What is instruction tuning and why does it matter?

Instruction tuning is supervised fine-tuning on (instruction, response) pairs. The pretraining objective (next-token prediction over raw web text) gives the model world knowledge and basic language modeling, but it doesn't teach the model to follow instructions, respond helpfully, or refuse. Instruction tuning bridges that gap by teaching Q->A style with curated prompts and high-quality answers. The 2022 FLAN paper showed that mixing many task families improves zero-shot generalization; modern open-instruct datasets (OpenHermes, Alpaca, Dolly, UltraFeedback) are the substrate for both base-model fine-tunes and the SFT stage of RLHF/DPO. Key trade-offs: (1) data quality > quantity: 10k high-quality SFT examples can beat 1M noisy ones; (2) too narrow instruction data overfits; (3) the SFT-then-DPO pipeline is standard. Senior nuance: "instruction pre-training" work shows that mixing instructions into pretraining can also help generalization.