Reinforcement Learning from Human Feedback (RLHF) is an AI technique where models learn from human guidance rather than only datasets or rules. The model is first trained on large data, then humans review its outputs, scoring them based on quality, relevance, or safety. These scores are converted into rewards, which the model uses to improve through reinforcement learning.
RLHF is used in large language models, chatbots, and content moderation tools, enabling AI to produce safer, more reliable, and user-friendly results that align with human preferences and expectations.
Jayant Shiv Narayana works at Macgence, specializing in AI Data Annotation, multilingual speech, text, image, and video annotation. He focuses on Human in the Loop methods to make datasets accurate, ethical, and diverse, helping AI teams build smarter and fairer models.
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