RLHF (Reinforcement Learning from Human Feedback)

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Reinforcement Learning from Human Feedback (RLHF) is an approach in artificial intelligence that combines reinforcement learning (RL) with human-provided feedback to improve the learning process of AI agents. RLHF recognizes that direct RL training, especially in complex and uncertain environments, can be time-consuming and challenging. By leveraging human feedback, RLHF seeks to accelerate the learning process and guide the AI agent toward better decision-making strategies.


Human feedback can come in various forms, such as reward signals, rankings, or demonstrations. This feedback provides valuable guidance to the AI agent, helping it understand desired behaviors and avoid unnecessary exploration of suboptimal actions. Combining human feedback with RL techniques can lead to more efficient and effective learning outcomes, enabling AI agents to learn from expert knowledge and real-world experience simultaneously.

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