RLHF (Reinforcement Learning with Human Feedback)

Reinforcement Learning with Human Feedback (RLHF) is a technique that trains AI models by incorporating human feedback into the reinforcement learning process. Instead of relying solely on algorithmic rewards, RLHF refines AI responses based on human preferences and ethical considerations, improving model alignment with human expectations. It plays a key role in training AI chatbots, recommendation systems, and decision-making models.

Key Features:

• Human-guided training – Incorporates direct human feedback into AI learning.

• Improved AI alignment – Reduces bias and refines model behavior.

• Iterative learning – Continuously improves based on human evaluations.

• Ethical AI development – Helps create safer AI models.

Best Use Cases:

• Training AI chatbots like ChatGPT.

• Improving content moderation systems.

• Enhancing recommendation engines.

• Refining self-driving car decision-making.