Author(s): Chew Loong Nian – AI ENGINEER

Originally published on Towards AI.

Sebastian Raschka's New Repo Builds a DeepSeek-R1 Clone in 8 Chapters — and It Shouldn't Be This Simple

For the last year I have treated reasoning models the way most developers do — as something that happens behind a frontier lab’s walls. o1, DeepSeek R1, GPT-5 Thinking: you send a prompt, the model “thinks,” and a better answer comes back. The thinking part felt like a moat. Then I spent a weekend inside Sebastian Raschka’s new reasoning-from-scratch repository, and the moat turned out to be a fence. The entire capability reduces to three technique families, taught across 8 chapters of plain PyTorch, and the code runs on a laptop. After a week of doing this for a living, that is the most useful thing I have read all month.

The article reviews Sebastian Raschka’s “reasoning-from-scratch” repository as an unusually readable, runnable (even on consumer hardware) guide to building a small reasoning model. After introducing why Raschka and the timing matter, it explains that the repo starts from a pre-trained base model (Qwen3) and adds reasoning through three main ideas: inference-time scaling (chain-of-thought, self-consistency, Best-of-N, and self-refinement), reinforcement learning with verifiable rewards using GRPO (including advanced GRPO variants and frontier-style training), and distillation (training a smaller student to imitate a teacher’s worked reasoning). It highlights a key surprise: the evaluation chapter is built to measure reasoning improvements reliably (e.g., building a verifier and using benchmarks like MATH-500) rather than relying on “magic.” It then gives practical instructions to run the notebooks, evaluate pre-trained checkpoints, and emphasizes who should read it and why—understanding beats access, because writing these methods down makes reasoning capability shared knowledge rather than a moat.

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Published via Towards AI