InstructGPT

InstructGPT takes a pretrained GPT-3 model and aligns it to user intent with a human-in-the-loop process. First it is fine-tuned on examples where humans demonstrate ideal responses, then a reward model is trained from human preference comparisons, and finally the model is optimized with reinforcement learning from human feedback so it prefers helpful, honest, and harmless answers. This shift from next-token prediction to instruction following reduces off-topic rambling, toxic outputs, and refusal mistakes, while improving step-by-step explanations and compliance with formatting requests. It laid the groundwork for later systems such as ChatGPT and the GPT-4 family by proving that preference data and careful alignment can produce models that feel cooperative and reliable in real use. Limitations remain, including sensitivity to annotator bias, occasional over-caution, and the need for clear prompts, but InstructGPT established the modern recipe for practical instruction-following LLMs.