OpenAI trains ChatGPT using Reinforcement Learning from Human Feedback (RLHF). An initial model is created by supervised fine-tuning. Human AI trainers play both sides (the user and AI assistant) in conversation and provide responses. Trainers also have access to model-written suggestions to help them compose their responses. This dataset with feedback is mixed with the InstructGPT dataset, transformed into a dialogue format.
To understand nuances of the language, its training is further refined using Proximal Policy Optimization. Several iterations of this process are performed. Feedback from the model’s performance in real conversations with users is also used. AI trainers rank different responses to form a reward model for reinforcement learning, helping ChatGPT understand text structures, context, and unspoken rules that govern human languages and dialogue.
However, there are still limitations in understanding and generating nuanced language. Ideally, future training should include more diverse data sets and more feedback from a wider audience to make the model capable of understanding and appropriately responding to more complex, nuanced dialogue.
