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    Build a Reasoning Model (From Scratch) (MEAP V01)

    Posted By: DexterDL
    Build a Reasoning Model (From Scratch) (MEAP V01)

    Build a Reasoning Model (From Scratch) (MEAP V01)
    English | 2025 | ISBN: 9781633434677 | 118 pages | PDF,EPUB | 9.58 MB


    Understand LLM reasoning by creating your own reasoning model–from scratch!

    LLM reasoning models have the power to tackle truly challenging problems that require finding the right path through multiple steps. In Build A Reasoning Model (From Scratch) you’ll learn how to build a working reasoning model from the ground up. Sebastian Raschka, the bestselling author of Build a Large Language Model (From Scratch), is your guide on this exciting journey. Sebastian mentors you every step of the way with clear explanations, practical code, and a keen focus on what really matters.

    In Build A Reasoning Model (From Scratch) you’ll learn how to:

    Implement core reasoning improvements for LLMs
    Evaluate models using judgment-based and benchmark-based methods
    Improve reasoning without updating model weights
    Use reinforcement learning to integrate external tools like calculators
    Apply distillation techniques to learn from larger reasoning models
    Understand the full reasoning model development pipeline

    Reasoning models break problems into steps, producing more reliable answers in math, logic, and code. These improvements aren’t just a curiosity–they’re already integrated into top models like Grok 4 and GPT-5. Build A Reasoning Model (From Scratch) demystifies these complex models with a simple philosophy: the best way to learn how something works is to build it yourself! You’ll begin with a pre-trained LLM, adding and improving its reasoning capabilities in ways you can see, test, and understand.
    about the book
    In Build a Reasoning Model (From Scratch), acclaimed ML research engineer Sebastian Raschka takes you inside the black box of reasoning-enhanced LLMs. You’ll start with a compact, pre-trained base model that runs on consumer hardware, then upgrade it step-by-step to tackle ever-more difficult problems and scenarios. You’ll measure its performance, add reasoning at inference time without training, and then improve it further with reinforcement learning. By the end of the book, you’ll have a small but capable reasoning stack built from the ground up!