LLM Alignment: Reinforcement Learning from Human Feedback (RLHF)

Aligning large language models to be helpful, honest, and harmless is one of the most critical challenges in modern AI development. Reinforcement Learning from Human Feedback (RLHF) is the core methodology used to guide raw models into becoming capable assistants. Through this text-based course, you will learn how to align and fine-tune open-weights models like Llama, starting from fundamental concepts and moving through the entire alignment pipeline. You will develop a clear understanding of reward models, policy optimization, and modern model evaluation. What you'll learn: - Understand the foundational concepts of LLM alignment and why reinforcement learning is necessary. - Configure reward models to capture human preferences and guide model behavior. - Apply policy optimization techniques to fine-tune open-weights models. - Evaluate model performance and safety using standard alignment metrics. - Compare RLHF with alternative modern alignment strategies like Direct Preference Optimization (DPO). This course begins with essential terminology and the theory behind human preference data before guiding you through the step-by-step process of training a reward model and optimizing your LLM. It is designed for software developers, data scientists, and AI beginners who want to understand how modern language models are trained for safety and utility. No prior experience with reinforcement learning is required. Start reading today to unlock the core techniques behind modern AI alignment.