Dan Fitzgerald

Reinforcement Learning from Human Feedback (RLHF) is a subfield of reinforcement learning (RL) in artificial intelligence that involves learning from human feedback instead of traditional reward signals. In RLHF, instead of providing a reward function that guides an agent's behavior, a human teacher provides feedback in the form of evaluations, suggestions, or corrections to the agent's actions. This feedback is used to improve the agent's decision-making and behavior.

What is Reinforcement Learning from Human Feedback (RLHF)?

ChatGPT is a large language model created by OpenAI that is based on the GPT-3.5 architecture. As a language model, ChatGPT is designed to understand natural language and generate responses to questions or prompts in a way that is human-like and informative. ChatGPT has been trained on a vast corpus of text data, allowing it to draw upon a broad range of knowledge to provide insights and answer questions. With its advanced natural language processing capabilities, ChatGPT can engage in conversations with users and provide helpful responses on a wide range of topics. Overall, ChatGPT is a sophisticated language model that can provide valuable information and insights to users across a wide range of applications.