<ul><li>Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning large language models with human intent, seen in models like ChatGPT and Claude.</li><li>RLHF uses human feedback to guide models towards desired behavior, moving beyond traditional supervised learning.</li><li>RLHF involves a three-phase pipeline that enables models to iteratively improve based on human-preferred behavior.</li><li>Through techniques like Proximal Policy Optimization (PPO), RLHF optimizes language models by generating responses aligned with human preferences.</li></ul>

A Comprehensive Introduction to Reinforcement Learning from Human Feedback (RLHF)

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