LLM Fine-Tuning and Alignment: SFT, RLHF, and DPO

Pre-trained large language models are incredibly powerful, but adapting them to specific tasks and ensuring they remain safe and reliable requires specialized post-training techniques. Understanding how to guide and constrain these models is a vital skill for modern AI developers and practitioners. This text-based course provides a clear pathway through the core concepts and workflows of model adaptation and safety. You will transition from understanding raw, next-token prediction to mastering the exact methodologies used to shape state-of-the-art conversational agents. What you'll learn: - Understand the foundational differences between pre-training, supervised fine-tuning (SFT), and human preference alignment. - Explore Reinforcement Learning from Human Feedback (RLHF) and how reward models shape model behavior. - Master Direct Preference Optimization (DPO) as a simplified, highly efficient alternative to traditional RLHF. - Learn the principles of Constitutional AI to implement self-correcting and rule-governed model safety. - Discover parameter-efficient fine-tuning (PEFT) methods like LoRA to adapt models with minimal computing resources. - Evaluate aligned models using modern safety and utility benchmarks to measure performance. Starting with key definitions and foundational concepts, the curriculum guides you through step-by-step written explanations and practical code-based representations of data preparation and training loops. This course is designed for software developers, data analysts, and AI enthusiasts eager to understand how modern models are steered, with no prior advanced machine learning experience required. Start reading today to master the essential techniques behind safe and effective language models.