Building a Reasoning Model from Scratch: Chinese Localization Project of Sebastian Raschka's Classic Tutorial

Recently, the reasoning-from-scratch-zh project initiated by community contributor xbsheng has emerged on GitHub. It aims to fully translate the new book Build a Reasoning Model (From Scratch) by Sebastian Raschka, an authority in machine learning education, into Chinese, helping Chinese developers learn the principles of building reasoning-based large language models. The project features an automated synchronization and incremental translation mechanism to ensure the Chinese version keeps up with updates to the original book in a timely manner.

Sebastian Raschka is an authority in machine learning education, and his previous work Build a Large Language Model (From Scratch) has become a classic for LLM beginners. In 2025, Manning released the sequel Build a Reasoning Model (From Scratch), which focuses on the implementation of reasoning models. However, the original English version presents a language barrier for Chinese developers. This project was created to address this issue—it not only provides a complete Chinese translation but also establishes an automated synchronization and incremental translation mechanism to ensure the timeliness of the version.

The translation strategy is strict: only Markdown cells (including titles, descriptions, and comments) are translated; code, LaTeX formulas, and URLs remain unchanged. Terminology uses the bilingual format "Chinese (English)" (e.g., "强化学习 (Reinforcement Learning)"). Automated synchronization is implemented via GitHub Actions: check for updates in the upstream repository at 10 AM daily → locate changed Markdown cells → perform incremental translation → generate a PR waiting for manual review and merging, forming a closed-loop process.

The project fully covers the core chapters of the original book and provides a clear learning path:

Chapter	Topic	Key Content
Chapter 2	Pre-trained LLM Text Generation	Text generation using the Qwen3 base model
Chapter 3	Reasoning Model Evaluation	Establish evaluation framework and benchmark tests
Chapter 4	Inference-time Scaling	Improve model performance by increasing inference computation
Chapter 5	Self-Refinement Mechanism	Iterative self-improvement strategy
Chapter 6	Reinforcement Learning Training	Train reasoning ability using RL
Chapter 7	GRPO Optimization	Improve the efficiency of reinforcement learning algorithms
Chapter 8	Model Distillation	Transfer reasoning ability to smaller models
In addition, it includes advanced appendix content such as Qwen3 source code analysis and large-scale LLM usage.

The translation uses an OpenAI-compatible API, which can flexibly connect to OpenAI, Azure OpenAI, or local open-source models. Incremental translation accurately identifies parts that need re-translation by comparing file hashes/cell contents, reducing API costs. Contributors should pay attention to review points: accuracy of technical terms, fluency of long sentences, and consistency of formatting (punctuation, spaces, code blocks, etc.).

This project fills the gap in systematic learning materials for reasoning models in the Chinese community, allowing Chinese developers to learn cutting-edge knowledge (such as the principles of reasoning models like DeepSeek-R1 and OpenAI o1/o3) synchronously without waiting for the official Chinese version. Its automated synchronization mechanism provides a reusable paradigm for open-source document localization, helping other projects achieve efficient and sustainable localization.

Developers can participate/use the project in the following ways: 1. Visit the GitHub repository to read the complete Chinese tutorial; 2. Feedback translation issues via Issues/PRs; 3. Clone the repository to learn Jupyter Notebooks by chapter; 4. Watch the repository to receive update notifications. The project follows the Apache-2.0 license, consistent with the original book's code repository, ensuring compliance.

reasoning-from-scratch-zh is not just a translation project; it is a model of the Chinese open-source community proactively lowering learning barriers and accessing cutting-edge knowledge. Its automated synchronization mechanism demonstrates an efficient content localization workflow combining CI/CD tools and LLM APIs, making it a high-quality learning resource for in-depth understanding of reasoning model principles.