AI Model Alignment: Training Neural Networks with RLHF and DPO

Supervised fine-tuning is only the first step in building capable AI systems. To make neural networks truly helpful, harmless, and aligned with human values, modern AI practitioners rely on advanced post-training alignment methodologies. This text-only course guides you through the core concepts and practical workflows of model alignment. You will transition from understanding basic supervised training to implementing state-of-the-art techniques that shape model behavior based on human preferences. What you'll learn: Understand the fundamental limitations of supervised fine-tuning and why alignment is necessary; Master the mechanics of Reinforcement Learning from Human Feedback (RLHF), including reward modeling and policy optimization; Implement Direct Preference Optimization (DPO) as a streamlined, stable alternative to traditional RL-based workflows; Compare PPO and DPO approaches to select the right alignment strategy for your specific use cases; Apply safety guardrails and evaluation metrics to measure model helpfulness and harmlessness. The course begins with foundational concepts of human preference learning, establishing key terminology before moving into detailed walkthroughs of reward modeling, policy optimization, and modern direct optimization code patterns. You will read through clear explanations, conceptual breakdowns, and practical implementation snippets. This course is designed for software developers, data scientists, and AI enthusiasts who have a basic understanding of neural networks and want to specialize in model post-training. Start reading today to master the essential techniques powering modern aligned AI models.