Reinforcement Learning from Human Feedback (RLHF) enhances AI by aligning it more closely with human values and preferences, ensuring model outputs are coherent and useful.
Integrating human judgment into the AI training process through RLHF creates a feedback loop where human evaluators influence the model's behavior, refining responses based on real-world expectations.
Traditional training techniques for AI models primarily focused on pre-training and fine-tuning; RLHF adds a crucial third stage that incorporates human insights directly into the learning process.
The rise of robotics in daily life illustrates the need for reliable AI; their integration into various tasks underscores the importance of aligning AI behavior with human needs and safety.
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