Part 10/15:
While initial GPT models predicted words adaptively, further improvements involved:
Supervised Fine-tuning (SFT): Training models on high-quality datasets with explicit instructions enhanced their ability to follow human instructions.
Reinforcement Learning with Human Feedback (RLHF): This step added a structured reward signal from human preferences, guiding models toward more desirable responses. The process involves collecting preference data, training a reward model, and optimizing the language model via reinforcement learning algorithms like Proximal Policy Optimization (PPO).
This methodology resulted in ChatGPT, which not only generates fluent language but also adheres to desired behavior—being helpful, honest, and harmless.