Early Feedback on “Build a Reasoning Model (From Scratch)”

#llm #reasoning #ai

A feedback on the book “Build a Reasoning Model (From Scratch)” from Manning edition by Sebastian Raschka.

Introduction and Disclaimer

As a devoted reader with a passion for technology, I’m always adding new books to my library, especially those related to AI and LLMs. The latest addition is “Build a Reasoning Model (From Scratch)” by Sebastian Raschka.

I’m a big fan of his work, and while I have no affiliation with him or Manning Publications, I wanted to share my initial thoughts on what I’ve read so far. I believe this book is a must-read for anyone looking to go “behind the scenes” with AI. It takes a hands-on approach, moving beyond the theory of reasoning in LLMs to show you how to add this capability yourself, step-by-step, in code. It’s not a guide to production deployment but rather a tour of the machinery that powers reasoning, and by the end, you’ll have built a reasoning model from scratch.

Chapters available so far…

Introduction and Chapter 1: Understanding Reasoning Models

The book Build a Reasoning Model (From Scratch), is a hands-on guide to developing reasoning capabilities in large language models (LLMs). It is geared toward LLM engineers, machine learning researchers, and developers. The author defines reasoning in the context of LLMs as the ability to produce intermediate steps before providing a final answer, a process often described as “chain-of-thought” (CoT) reasoning. While LLMs can simulate logical reasoning through learned statistical patterns, they don’t apply explicit, rule-based logic. The book will teach how to improve these implicit reasoning capabilities.

Chapter 2: Generating Text with a Pre-trained LLM

This chapter serves as a foundation for the rest of the book by providing the technical setup and essential LLM concepts. It explains the process of text generation, which is a sequential (autoregressive) process where the model predicts one token at a time. You will learn how to:

Set up a coding environment and install necessary dependencies.
Load a pre-trained base LLM, such as Qwen3 0.6B.
Use a tokenizer to convert text into token IDs and back again.
Improve text generation speed using techniques like
KV caching and model compilation.

Approaches to Improving LLM Reasoning

The book outlines three main approaches for enhancing an LLM’s reasoning capabilities, which are applied after the conventional pre-training and post-training stages:

Inference-time compute scaling: This method improves a model’s reasoning during inference (when a user prompts it) without changing the model’s underlying weights. It involves trading increased computational resources for better performance through techniques like chain-of-thought reasoning and various sampling procedures.
Reinforcement learning (RL): This approach updates the model’s weights during training by encouraging actions that lead to high reward signals. Unlike Reinforcement Learning with Human Feedback (RLHF), which is used for preference tuning and relies on human evaluations, RL for reasoning models often uses automated, objective reward signals, such as correctness in math problems.
Supervised fine-tuning and model distillation: This technique transfers complex reasoning patterns from larger, more powerful models to smaller, more efficient ones. This is a method for improving a model’s reasoning capabilities by leveraging the strengths of a superior model.

Conclusion

Having already explored the introductory and foundational chapters, I’m more excited than ever to dive into the rest of the book. The initial chapters have already provided a clear roadmap for enhancing LLM reasoning, from inference-time compute scaling to reinforcement learning and supervised fine-tuning. As a long-time follower of the author’s work, I’m confident that the upcoming chapters will provide the practical, hands-on knowledge needed to truly understand these advanced techniques.