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“Reinforcement Learning from Human Feedback” is a category for books that explore how AI systems are trained to align with human preferences by learning from human judgments rather than only from fixed datasets or hand-coded rules. Titles in this genre cover the full pipeline—from collecting and curating human annotations and preference comparisons, to building reward models and optimizing policies, to evaluating helpfulness, safety, and fairness in real-world use. Readers will find foundations and algorithms, practical guides to data practices and tooling, case studies on assistants and instruction-following models, and discussions of risks like bias, misuse, and overfitting to feedback. The category is inherently interdisciplinary, bridging machine learning, human–computer interaction, psychology, and ethics, and is relevant to researchers, practitioners, product teams, and policymakers who want to understand, apply, or govern techniques that make AI systems more aligned, reliable, and responsible.

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