The introduction to Stanford’s CS329H course outlines its focus on integrating human preferences into machine learning through interdisciplinary approaches, practical projects, and ethical considerations, emphasizing interactive learning and real-world applications. It highlights key topics such as preference modeling, reinforcement learning, and AI alignment, preparing students to address both technical challenges and societal impacts in this evolving field.
The introduction to Stanford’s CS329H course, “Machine Learning from Human Preferences,” presented by faculty member Sammy Coua, outlines the course’s focus on the intersection of machine learning and human feedback. This is the second offering of the course, building on lessons learned from the previous year, with a strong emphasis on interactive learning from human preferences. The course aims to explore foundational concepts and practical strategies for eliciting and embedding human values and preferences into AI models, drawing from diverse fields such as economics, psychology, and statistics. Students will engage through lectures, homework, projects, and invited talks, with a significant portion of the grade based on homework and active participation.
The course content is structured around four main modules: modeling human choice, model-based preference learning, model-free optimization, and human values with AI alignment. A unique aspect of the course is the availability of a newly developed textbook, which is still in draft form and open to student feedback. The course encourages collaborative projects, allowing students to work in groups to explore various applications of learning from human preferences, ranging from language models to robotics and recommendation systems. Ethical considerations and societal impacts of AI systems trained on human preferences are integral to the curriculum, highlighting the importance of representation, bias, and fairness in AI development.
A significant portion of the discussion centers on the practical challenges and methodologies of learning from human preferences, particularly in language models. The course covers the typical pipeline involving supervised fine-tuning with human-labeled data, preference comparisons, and reinforcement learning to optimize model behavior according to human feedback. The instructor emphasizes the importance of interactive querying to efficiently gather high-quality human preference data, which is often costly and complex to obtain. The course also addresses the limitations and open research questions in this area, such as the role and necessity of explicit reward models versus implicit preference learning methods like Direct Preference Optimization (DPO).
Applications beyond language models are also explored, including exoskeleton calibration for personalized assistance, classification problems with asymmetric costs, and recommendation systems. The course highlights real-world examples where human preferences directly influence AI performance and decision-making. Challenges such as reward hacking, human inconsistency, and the ethical implications of outsourcing preference data collection are discussed to provide a comprehensive understanding of the field. The instructor stresses the evolving nature of this research area and encourages students to contribute through projects and discussions.
Overall, CS329H aims to provide students with a broad yet foundational understanding of how human preferences can be integrated into machine learning systems. It balances technical depth with interdisciplinary perspectives and ethical considerations, preparing students to tackle complex problems in AI alignment and human-centered machine learning. The course is designed for students with a basic background in machine learning and programming, and it fosters an interactive learning environment where feedback and adaptation are key components. The introduction sets the stage for a dynamic and evolving exploration of machine learning from human preferences, emphasizing both theoretical and practical challenges.