Instruction tuning fine-tunes on (instruction, response) pairs — teaches the model to follow directions.

Instruction tuning fine-tunes on (instruction, response) pairs — teaches the model to follow directions. RLHF: human raters rank outputs, a reward model learns preferences, the LLM is fine-tuned via PPO to maximize reward. RLHF reduces harmful outputs and improves helpfulness beyond what supervised instruction tuning alone achieves.