Foundations of RLHF: Building an AI Alignment Loop

How do we ensure large language models behave in ways that are safe, helpful, and aligned with human values? Reinforcement Learning from Human Feedback (RLHF) is the key methodology behind training modern conversational AI. This text-only course guides you through the fundamental concepts and practical steps of a classic alignment pipeline, transitioning from basic theory to reading and analyzing the code structures required to build your own feedback loop. You will learn to: Understand the foundational concepts of AI alignment and why human feedback is critical; Implement Supervised Fine-Tuning (SFT) to prepare a base language model for alignment; Build and train a reward model based on human preference datasets; Configure the Proximal Policy Optimization (PPO) loop to update model behavior; Explore modern alternatives like Direct Preference Optimization (DPO) for streamlined alignment; Evaluate aligned models using standard metrics to ensure safety and helpfulness. The course starts with essential definitions of reinforcement learning and alignment terminology before progressing step-by-step through SFT, reward modeling, and PPO optimization, concluding with modern optimization techniques and evaluation strategies. This course is designed for developers and AI enthusiasts new to alignment techniques, requiring no prior experience with reinforcement learning. Start reading today to unlock the mechanics behind state-of-the-art AI alignment.