Reinforcement learning from human feedback

Reinforcement learning is a way for a computer program to learn how to make good decisions on its own. Imagine you had a robot that had to get through a maze. At first, the robot will make a lot of mistakes and run into walls, but as it gets more experience, it will learn the right way to navigate the maze.

But sometimes, we want the robot to learn faster and have a human give it feedback on what it's doing right and wrong. This is where reinforcement learning from human feedback comes in. The human will tell the robot when it makes a good decision and reward it with something positive like a pat on the back or a cookie. When the robot makes a bad decision, the human will let it know and give it a negative consequence like a slap on the wrist or taking away a cookie.

The robot will then use this feedback to adjust its decision-making algorithm and hopefully make better choices next time. By doing this over and over again, the robot can become very good at making decisions and completing tasks.

So basically, reinforcement learning from human feedback is like training a robot to do something by giving it rewards and punishments for the things it does right and wrong.