VOOZH about

URL: https://www.amazon.com/dp/1633434672/ref=mes-dp

⇱ Build a Reasoning Model (From Scratch): 9781633434677: Computer Science Books @ Amazon.com

👁 Image
👁 Image
Other New from $55.91

Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required.

Read instantly on your browser with Kindle for Web.

Using your mobile phone camera - scan the code below and download the Kindle app.

👁 QR code to download the Kindle App

  • Image Unavailable

    Image not available for
    Color:

Follow the author

Get new release updates & improved recommendations
Something went wrong. Please try your request again later.

OK

Build a Reasoning Model (From Scratch)

by Sebastian Raschka (Author) Format: Paperback
Savings Pre-order Price Guarantee. Terms
{"desktop_buybox_group_1":[{"displayPrice":"$55.91","priceAmount":55.91,"currencySymbol":"$","integerValue":"55","decimalSeparator":".","fractionalValue":"91","symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"qJFvxLWoUe8pYSF5zzR9jzVwzMgBbcf9Y7r3tCc2aZQsE8cSnILkJ1rIivpA0eQcCGgG49QavzM6%2BRoufuph48x9orIV%2FXNDK2F3%2BrxklJEbpy44uRz7f7K5f4zZFIsWkggRnK81wOqBgJQS6XhLHw%3D%3D","locale":"en-US","buyingOptionType":"NEW","aapiBuyingOptionIndex":0}]}

Purchase options and add-ons

Get the eBook free when you register your print book at Manning.

"An exceptional deep dive into the next frontier of AI.”
—Aman Chadha, Google

This book 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. Readers 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. Difficult concepts such as softmax, temperature, and top-p sampling are clarified with code-linked explanations and diagrams, and visual workflows make pipelines and scoring methods easier to follow.

Reading the book feels like following a guided technical build rather than a loose survey of AI topics. Each concept is introduced because the project now needs it. Diagrams, roadmaps, code listings, exercises, and repeated workflow summaries help readers stay oriented through advanced material. This structure reflects Sebastian Raschka’s professional strength: explaining complex machine learning topics by making every detail concrete and showing exactly where each section fits in the larger story. He does not treat mechanisms like evaluation, log-probabilities, KL regularization, or distillation as isolated abstractions; he connects them to the goal of making reasoning models understandable and implementable.

Physically and organizationally, the book has eight chapters and seven substantial appendixes. That design keeps the main narrative focused while moving supporting material like references, exercise solutions, model source code, larger models, batching, evaluation alternatives, and chat interfaces into ordered appendixes. The result is a logically flowing book that remains hands-on, navigable, and technically deep without constantly interrupting the central build.

What's inside

• From-scratch implementations of core LLM reasoning improvements
• Verifier-based evaluation methods
• RL with automatic verifiers for mathematics tasks

About the reader

For readers 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).

Table of Contents

1 Understanding reasoning models
2 Generating text with a pretrained LLM
3 Evaluating reasoning models
4 Improving reasoning with inference-time scaling
5 Inference-time scaling via self-refinement
6 Training reasoning models with reinforcement learning
7 Improving GRPO for reinforcement learning
8 Distilling reasoning models for efficient reasoning
A References and further reading
B Exercise solutions
C Qwen3 LLM source code
D Using larger LLMs
E Batching and throughput-oriented execution
F Common approaches to model evaluation
G Building a chat interface
👁 Image
Report an issue with this product or seller
  1. ISBN-10
    1633434672
  2. ISBN-13
    978-1633434677
  3. Publisher
    Manning
  4. Publication date
    August 11, 2026
  5. Language
    English
  6. Print length
    440 pages
See all details

Frequently bought together

This item: Build a Reasoning Model (From Scratch)
$55.91$55.91
Get it as soon as Friday, Aug 14
This title will be released on August 11, 2026.
Pre-order now.
Ships from and sold by Amazon.com.
Build a Large Language Model (From Scratch)
$46.77$46.77
Get it as soon as Tuesday, Jul 14
Sold by Burlington MA- Used Book Superstore -new books too and ships from Amazon Fulfillment.
Reinforcement Learning from Human Feedback: Alignment and post-training of LLMs
$59.99$59.99
Get it as soon as Friday, Jul 31
This title will be released on July 28, 2026.
Pre-order now.
Ships from and sold by Amazon.com.
Total price: $00$00
To see our price, add these items to your cart.
Try again!
Details
Added to Cart
Some of these items ship sooner than the others.
Choose items to buy together.

Customers who viewed this item also viewed

Page 1 of 1 Start over
  5. Reinforcement Learning from Human Feedback: Alignment and post-training of LLMs
    Nathan Lambert
    Paperback
    Get it as soon as Friday, Jul 31
    FREE Shipping by Amazon
    This title will be released on July 28, 2026.

Customers also bought or read

Page 1 of 1Start over
  26. Causal AI
    Paperback
    $51.86$51.86
    FREE delivery Fri, Jul 3
Loading...

From the Publisher

👁 Build a Reasoning Model (From Scratch) quote 1

"Distills the profound ideas in the clearest, most accessible way."

Byron Hsu, LMSYS

👁 Build a Reasoning Model (From Scratch) quote 2

“Big. Dope. Great read, fun writing."

Chris Alexiuk, NVIDIA

👁 Build a Reasoning Model (From Scratch) quote 3

“The gold standard for developers wanting to build at the cutting edge of AI."

Omar Sanseviero, Author of Hands-On Generative AI with Transformers and Diffusion Models

why this book?

Build a Reasoning Model (From Scratch) helps you understand modern reasoning-oriented LLMs by building their core techniques from scratch rather than treating them as black boxes.

The book gives practical experience with evaluation, inference-time scaling, self-consistency, self-refinement, reinforcement learning with verifiable rewards, GRPO, and distillation.

Readers who know Python and some machine learning will gain a clear, hands-on path from using a pretrained LLM to improving, training, and evaluating reasoning models.

about Manning

Manning helps developers and tech professionals stay ahead in a fast-moving industry with expert-led books, videos, and projects. Learning never stops, but it’s hard to keep up, so we focus on content that’s practical, clear, and trusted. As an independent publisher, we adapt quickly, from pioneering early-access books to offering DRM-free eBooks. Our series, like "In Action" and "In a Month of Lunches", reflect a commitment to making complex topics accessible.

Deep Learning with Python, Third Edition
AI Agents in Action
Build a Large Language Model (From Scratch)
LLMs in Production: From language models to successful products
Data Analysis with LLMs: Text, tables, images and sound (In Action)
Causal AI
Customer Reviews
4.3 out of 5 stars 35
4.0 out of 5 stars 47
4.5 out of 5 stars 543
4.5 out of 5 stars 34
4.8 out of 5 stars 6
4.4 out of 5 stars 14
Price $72.80$72.80 $41.64$41.64 $49.24$49.24 $50.66$50.66 $36.49$36.49 $51.86$51.86
Level of proficiency Intermediate Intermediate Intermediate Intermediate Intermediate Advanced
About the reader For readers with intermediate Python skills. For intermediate Python programmers. Readers need intermediate Python skills and some knowledge of machine learning. For data scientists and ML engineers. For data scientists and data analysts. For data scientists and machine learning engineers.
Special features Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant.
Pages 648 344 368 456 232 520

Editorial Reviews

About the Author

Sebastian Raschka has been working on machine learning and AI for more than a decade. Sebastian joined Lightning AI in 2022, where he now focuses on AI and LLM research, developing open-source software, and creating educational material. Prior to that, Sebastian worked at the University of Wisconsin-Madison as an assistant professor in the Department of Statistics, focusing on deep learning and machine learning research. He has a strong passion for education and is best known for his bestselling books on machine learning using open-source software.

Product details

Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Product Videos

👁 Image
Manning Publications-Veiled Access
Customer ReviewFirst Chapter SummarySee Full Review

About the author

Follow authors to get new release updates, plus improved recommendations.
Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Sebastian Raschka, PhD is an LLM Research Engineer with over a decade of experience in artificial intelligence. His work bridges academia and industry, including roles as senior engineering staff at an AI company and a statistics professor.

As an independent researcher and industry expert, Sebastian collaborates with companies on AI solutions and serves on the Open Source Advisory Board at University of Wisconsin–Madison.

Sebastian specializes in LLMs and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations.

Related books

Page 1 of 1Start Over
Sponsored

Customer reviews

  • 5 star
    0%
  • 4 star
    0%
  • 3 star
    0%
  • 2 star
    0%
  • 1 star
    0%
How customer reviews and ratings work

Customer Reviews, including Product Star Ratings help customers to learn more about the product and decide whether it is the right product for them.

To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzed reviews to verify trustworthiness.

Learn more how customers reviews work on Amazon
There was a problem filtering reviews. Please reload the page.
There are 0 customer reviews.
👁 Build a Reasoning Model (From Scratch)