Reinforcement Learning from Human Feedback
Summary
RLHF has become an important tool for deploying cutting-edge ML systems, combining human feedback with reinforcement learning. The article provides a gentle introduction to core methods, tracing origins across disciplines and detailing the end-to-end optimization pipeline from instruction tuning to reward modeling and direct alignment. It also discusses advanced topics like synthetic data and evaluation for open questions in the field.