LLM Post-Training: Fine-Tuning and Reinforcement Learning Basics

Pre-trained large language models are powerful, but adapting them to specific tasks and aligning them with human preferences requires post-training. Understanding how to guide these models is essential for building safe, reliable, and specialized AI applications. In this text-based course, you will learn the fundamental concepts and practical workflows behind LLM post-training, moving from raw models to helpful, aligned AI assistants. What you'll learn: - Understand the key differences between pre-training, supervised fine-tuning (SFT), and reinforcement learning. - Apply parameter-efficient fine-tuning (PEFT) methods like LoRA to adapt models with minimal computational resources. - Explore Reinforcement Learning from Human Feedback (RLHF) and modern alignment alternatives like Direct Preference Optimization (DPO). - Evaluate model behavior and safety to ensure outputs are helpful, honest, and harmless. - Analyze code snippets and written walkthroughs to prepare datasets for custom fine-tuning tasks. The course begins with foundational definitions of post-training paradigms before guiding you through data preparation, fine-tuning configurations, and alignment strategies. You will progress from theoretical concepts to reading and analyzing real-world implementation code. This course is designed for software developers, data enthusiasts, and AI beginners who want to understand how LLMs are customized. No prior experience with advanced machine learning is required, though basic Python familiarity is helpful. Start reading today to unlock the power of custom model alignment and post-training.