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Key Takeaways
- RLHF uses human feedback to make large language models sound more accurate, helpful, and natural.
- The process starts with human-created prompts and supervised fine-tuning of model responses.
- A reward model then helps the AI evaluate and improve its own outputs over time.
- RLHF is used beyond text, including in robotics, games, and other generative AI systems.
- While powerful, RLHF is limited by the subjectivity and potential bias of human feedback.

