"Build a Reasoning Model (From Scratch)" is a practical guide to understanding how modern reasoning-oriented LLMs work by building their core methods step by step. The book tells a clear engineering story: start with a conventional pre-trained LLM, learn how text generation works, build reliable evaluation tools, improve reasoning through inference-time methods, then move into training-based approaches such as reinforcement learning and distillation.

The progression is deliberate. Early chapters establish the baseline model and explain text generation, KV caching, and evaluation with math verifiers. The middle chapters show how reasoning can be improved without changing model weights, using chain-of-thought prompting, sampling, self-consistency, response scoring, and self-refinement. Later chapters move to changing the model itself through reinforcement learning with verifiable rewards, GRPO improvements, format rewards, and finally distillation from stronger reasoning models into smaller ones.

The book is especially useful because it implements the core methods from scratch rather than treating them as black-box library calls. Listeners see how self-consistency, self-refinement, Best-of-N, and training-based methods actually work, including their cost and latency trade-offs. It also discusses common failure modes, including cases where refinement can make answers worse. Physically and organizationally, the book has eight chapters and seven substantial appendixes. The result is a logically flowing book that remains hands-on, navigable, and technically deep without constantly interrupting the central build.

About the listener: For listeners who know Python and have some knowledge of machine learning.

About the author: Sebastian Raschka is an LLM Research Engineer with over a decade of experience. He is the author of the bestselling book "Build a Large Language Model (From Scratch)."

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.