Video course to build an AI reasoning model from scratch
In brief
- Over 11 hours of videos to implement an AI reasoning model using Python and PyTorch.
- Covers evaluation, inference-time optimization, and reinforcement learning.
- Uses a pre-trained Qwen3 0.6B model and requires Python and PyTorch basics.
An instructor released a video course of over 11 hours to implement an AI reasoning model from scratch using Python and PyTorch. According to the publisher, the videos accompany a dedicated book and cover evaluation, inference-time optimization, reinforcement learning, and distillation.
The videos walk through setting up a development environment, loading a pre-trained model (Qwen3 0.6B), and adding mechanisms like KV caching and compilation to speed up text generation. They also include building a math answer verifier and exploring inference-time optimization techniques such as diverse answer generation and majority voting.
This content targets developers looking to understand reasoning models, their differences with classic LLMs, and their role in AI agents. Prerequisites include basic Python and PyTorch skills, with a GPU recommended for advanced experiments. Resources are available for free online, with a paid book for deeper study.
What it changes for you
- EducationEducators and students in AI now have a practical resource to teach or learn how to build reasoning models from scratch.
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