Reinforcement learning from human (or AI) feedback

In this episode of The Turing Talks, we dive into the intriguing world of Reinforcement Learning from Human Feedback (RLHF) and its role in training Large Language Models (LLMs). We break down the three key steps of RLHF: collecting human feedback, training a reward model, and optimizing AI systems for maximum rewards. Discover the advantages of RLHF in communicating complex goals without manual reward design, reducing the risk of reward hacking. We also address the challenges of obtaining quality feedback, reward model misspecification, and biases in policy optimization. Additionally, we introduce Constitutional AI (CAI) as a novel approach to improve RLHF, utilizing a set of human-written principles to enhance AI behavior. Join us for a comprehensive overview of RLHF, its limitations, and how CAI can lead to safer and more transparent AI development.