Reinforcement Learning from Human Feedback (RLHF) Explained

Explore Reinforcement Learning from Human Feedback (RLHF) in this 11-minute video from IBM. Dive into the key components of RLHF, including reinforcement learning, state space, action space, reward functions, and policy optimization. Understand how this technique refines AI systems, particularly large language models, by aligning outputs with human values and preferences. Learn about the three phases of RLHF: pretraining, fine-tuning, and reinforcement learning. Discover the limitations of RLHF and potential future improvements like Reinforcement Learning from AI Feedback (RLAIF). Gain insights into this crucial technique for enhancing AI systems and its impact on aligning artificial intelligence with human preferences.

Intro
What is RL
Phase 1 Pretraining
Phase 2 Fine Tuning
Limitations