One signal

Sebastian Raschka has released an eight-part video course and companion book on building a reasoning model from scratch. The materials cover inference scaling, reinforcement learning with verifiable rewards and distillation, using Python and PyTorch.

The course uses a pre-trained Qwen3 model and is designed as a practical follow-on to Raschka’s earlier LLM coding tutorials. For developers, it offers a structured route into techniques behind reasoning models, rather than another tour of AI buzzwords with the technical bits politely left outside. The course and book are described in Raschka’s announcement. Should learning to build these models start with hands-on implementation, or with a deeper grounding in the theory?

Discuss: Should learning to build reasoning models start with hands-on implementation, or with a deeper grounding in the theory?

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