How does Reinforcement Learning from Human Feedback (RLHF) work in NLP models?

RLHF aligns language models with human preferences using reward modeling and reinforcement learning.

RLHF involves three stages: supervised fine-tuning, training a reward model from human comparisons, and optimizing the policy using PPO or similar RL algorithms. It improves helpfulness and safety but introduces challenges like reward hacking and instability in training dynamics.