Reinforcement Learning from Human Feedback - A Review

Abstract

Reinforcement Learning from Human Feedback (RLHF) is a burgeoning field at the intersection of artificial intelligence and human interaction. This approach involves training models to make decisions in dynamic environments by iteratively receiving feedback from human evaluators. In this process, initial models interact with the environment, and human evaluators provide feedback on the model's actions. The model is then updated based on this feedback, enhancing its decision-making capabilities over time.  RLHF  is  particularly  valuable  in  scenarios  where  predefine d  rules  may  be inadequate, emphasizing adaptability and learning from real-world experiences. This abstract explores the applications, advantages, and challenges of RLHF, highlighting its promising results in domains such as robotics, gaming, and natural l anguage processing. The collaboration between machine learning algorithms and human intuition in RLHF presents a compelling synergy that addresses complex problems more effectively than traditional methods. As technology advances, RLHF is poised to unlock new possibilities and drive innovations across diverse fields.