Simple definition of Reinforcement Learning from Human Feedback (RLHF)
Reinforcement Learning from Human Feedback (RLHF) is a way to train AI using ratings from people. RLHF stands for Reinforcement Learning from Human Feedback: “reinforcement learning” means learning by trying actions and getting scores, and “human feedback” means people judge which answers are better. The AI is trained to prefer responses humans rate as helpful, safe, and clear. For everyday users, RLHF can make chatbots more polite and useful. A downside is that ratings can be inconsistent or biased, and the AI may learn to sound good even when it is not fully correct.
How to explain Reinforcement Learning from Human Feedback (RLHF) to kids
People look at the computer’s answers and tell it which ones are better. The computer learns to make more answers like the ones people liked. It is like learning from coaching and scorecards.
Here’s how to think about it
Imagine you are practicing a speech and your classmates vote on which version is clearest. You then practice the version that got the best votes. RLHF is like that voting and practice, but for an AI’s answers.