What is Reinforcement Learning from Human Feedback (RLHF)?

Reinforcement Learning from Human Feedback (RLHF) is a method of training AI models where human feedback is used as a source of reinforcement signals. Instead of relying solely on predefined reward functions, RLHF incorporates feedback from humans to guide the learning process.

How does RLHF work?

RLHF works by collecting feedback from humans on the agent's behavior and using this feedback to update the agent's policy. This can be done in various ways, such as by having humans rank different actions or by using human feedback to adjust the rewards of the reinforcement learning algorithm.

RLHF can help to overcome some of the limitations of traditional reinforcement learning, such as the difficulty of specifying a suitable reward function.

What are the applications and challenges of RLHF?

RLHF has potential applications in many areas where reinforcement learning is used, including robotics, game playing, and more. However, it also faces challenges. Collecting human feedback can be time-consuming and expensive, and there can be discrepancies between different human evaluators. Furthermore, it can be difficult to scale RLHF to complex tasks or large state spaces.