Reinforcement Learning in Generative Models focuses on improving AI-generated outputs by using reward-based learning techniques. In this approach, models learn by receiving feedback signals that guide them toward better performance over time. This training explains how reinforcement learning helps generative models refine text, images, or other outputs based on quality, relevance, and user preferences. It also covers key concepts such as rewards, policies, exploration, and optimization strategies. You will learn how reinforcement learning is applied to fine-tune large language models and improve alignment with human expectations. The course also highlights best practices for building more accurate, controlled, and adaptive generative AI systems.
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