How to Train a Reasoning LLM: From Blank Slate to Deep Thinking

Want to know how AI models go from basic text predictors to native reasoning engines? Learn to train an LLM from scratch and teach it to reason step-by-step. Ever wondered how modern AI models like DeepSeek-R1 or OpenAI's o-series handle complex math, coding, and logical analysis? In this deep-dive tutorial, we take you through the complete lifecycle of LLM training—from a blank slate to building advanced, native reasoning capabilities. Whether you are a machine learning engineer, a data scientist, or an AI enthusiast, you will learn the exact steps required to transform a base model into a deliberate, problem-solving system. In this video, we cover: 🧠 Pre-training Basics: How base models are trained to predict the next token from raw data. 🔗 Chain of Thought (CoT): Teaching the model to articulate its intermediate steps. ⚙️ Inference Time Compute: How hidden scratchpads and test-time compute scale logic. 🤖 Reinforcement Learning (RL): Using verifiable rewards to align and train-time and test-time reasoning.