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Raschka posts 11-hour video course on building reasoning models
Sebastian Raschka said Oct. 24 he released eight from-scratch coding videos, more than 11 hours, on evaluating and training reasoning models in Python and PyTorch.
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Machine-learning researcher Sebastian Raschka said Oct. 24 he posted eight coding lectures totaling more than 11 hours on how to evaluate and train reasoning models in Python and PyTorch1.
The series, published on his Ahead of AI newsletter, starts from a pre-trained Qwen3 0.6B model and implements evaluation, inference-time scaling, reinforcement learning with verifiable rewards, and distillation, he wrote1. Raschka described it as a sequel to last year’s Coding LLMs from the Ground Up videos, though he said that course is not a strict prerequisite1.
He framed a reasoning model as a conventional LLM with extra post-training, typically reinforcement learning with verifiable rewards and optional inference scaling, and as the engine inside an agent harness that handles tools and context1. He said most current-generation large language models are reasoning models or offer a reasoning mode1.
Early videos cover environment setup, loading Qwen3 weights, streaming generation, KV caching, and torch.compile1. Later lectures build a math-answer verifier, score the base model on MATH-500, then add temperature sampling, top-p filtering, self-consistency, log-probability scoring, and a self-refinement loop1.
Two reinforcement-learning videos implement a simplified Group Relative Policy Optimization loop for RLVR, then add clipping, KL terms, format rewards, and training diagnostics1. The final lecture distills from teacher answers Raschka said were generated with DeepSeek R1 and compares the result on MATH-5001.
He recommended Python and PyTorch fluency; CPU is enough for early generation and evaluation, while a GPU is more practical for training1. Setup notes live in his GitHub repository, he wrote1.
The videos accompany his book Build a Reasoning Model (From Scratch), sold by Manning and Amazon1. He also pointed to a shorter LinkedIn Learning overview of the same ideas1. He said he is taking a short break from newsletter research roundups and plans to return next month1.
