How ChatGPT is trained step by step?

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ChatGPT's training initiates with a foundational phase, where it undergoes pre-training on a massive dataset of text and code to learn language patterns and predict the next token. This general-purpose language model is then fine-tuned through Supervised Fine-Tuning (SFT), where human AI trainers provide demonstrations of desired conversational behavior by crafting prompt-response pairs. The subsequent crucial stage is Reinforcement Learning from Human Feedback (RLHF), designed to align the model with human preferences and instructions. Within RLHF, a reward model is trained by having human annotators rank several model-generated responses to various prompts. Finally, the main ChatGPT model is iteratively optimized using algorithms like Proximal Policy Optimization (PPO), leveraging the reward model to learn how to generate responses that achieve high human preference scores, resulting in more helpful, harmless, and honest outputs.