Sebastian Raschka Releases 11-Hour Reasoning Model Course | Zeniteq
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Sebastian Raschka Releases 11-Hour Reasoning Model Course
Eight coding videos walk developers through evaluating and improving a pretrained Qwen3 model with Python, PyTorch, inference-time scaling, reinforcement learning and distillation.
Sebastian Raschka released an 11-hour video course on October 24, 2026, teaching developers how to evaluate and improve a pretrained Qwen3 model using Python and PyTorch. The eight-chapter sequence covers inference-time scaling, reinforcement learning, and distillation techniques without retraining the base model from scratch. Early chapters run on CPUs; later experiments with reinforcement learning require GPU resources. The course progresses from text generation and performance measurement through chain-of-thought prompting, then into training methods like Group Relative Policy Optimization and knowledge distillation.
Sebastian Raschka published eight coding videos totaling more than 11 hours on October 24, 2026, giving developers a code-level introduction to building reasoning-model techniques. The course announcement describes a hands-on sequence that starts with a pretrained Qwen3 model and implements methods for evaluating and improving it.
This is a learning resource, not a new model release. Its practical appeal is the progression: generate text, measure performance, spend more computation on answering questions, then explore training methods that change the model itself.
The distinction matters for anyone planning to follow along. Early exercises can run on a CPU, while later experiments become more practical with a GPU. Familiarity with Python and PyTorch is recommended; completing Raschka’s earlier LLM course is optional.
Why the Course Starts With a Pretrained Model
Raschka’s use of “from scratch” refers to implementing reasoning methods in Python and PyTorch. It does not mean collecting a pretraining corpus and training an entire language model from randomly initialized weights.
The announcement makes that scope explicit: starting with a pretrained model keeps the work focused on evaluation, inference-time scaling, reinforcement learning and distillation. The companion repository likewise describes adding reasoning capabilities to an existing base LLM, step by step.
That is a sensible educational boundary. Base-model pretraining would introduce a separate set of questions about datasets, tokenization, training infrastructure and compute budgets. Here, learners can hold the starting model relatively constant and examine what a particular reasoning technique changes.
The resulting comparison is more useful than simply asking whether a model “can reason.” Does generating several answers improve accuracy? Can feedback improve a failed attempt? Can reward-based training make successful behavior more likely? Those are concrete experiments.
The videos accompany Raschka’s Build a Reasoning Model (From Scratch) book. He recommends reading a chapter, working through its code alongside the corresponding video and attempting the exercises. The materials are designed to stand alone from his earlier Build a Large Language Model (From Scratch) course.
Eight Stages, With a Practical Outcome for Each
The repository provides an eight-chapter sequence that makes the learning path clear. The table below maps those chapters to practical outcomes; it is a curriculum guide, not a list of independently verified video titles or runtimes.
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Understand what reasoning-model techniques add to a conventional LLM and where they fit in a larger system.
2. Generating Text With a Pretrained LLM
Loading and generation
Establish a working text-generation path using the pretrained model.
3. Evaluating Reasoning Models
Measuring outputs
Build an evaluation baseline before trying to improve performance.
4. Improving Reasoning With Inference-Time Scaling
Additional computation during generation
Explore chain-of-thought prompting and self-consistency while keeping model weights unchanged.
5. Inference-Time Scaling via Self-Refinement
Revising and selecting responses
Explore best-of-N and self-refinement approaches, then assess whether the additional generation helps.
6. Training Reasoning Models With Reinforcement Learning
Reward-driven training
Connect generated answers, verifiable rewards and model updates in a training loop.
7. Improving GRPO for Reinforcement Learning
Refining the training method
Examine improvements to Group Relative Policy Optimization and their implementation.
8. Distilling Reasoning Models for Efficient Reasoning
Learning from another model’s outputs
Implement a distillation workflow and examine its efficiency tradeoffs.
The repository includes main notebooks and exercise-solution notebooks for chapters 2–8. Chapter 1 is conceptual and has no main code.
I’d treat this sequence as an experimental dependency chain. Text generation gives you something to measure; evaluation gives later changes a meaningful reference point. Skipping directly to reinforcement learning risks producing a more complicated system without a reliable way to tell whether it improved.
Inference-Time Scaling Spends Compute Without Retraining
Chapters 4 and 5 explore a route to better answers that leaves the model’s weights unchanged. The additional work happens while producing or refining a response.
The repository’s optional materials identify several concrete techniques:
Chain-of-thought prompting: encourage intermediate reasoning steps before the final answer.
Self-consistency: generate multiple responses and compare their final answers, commonly selecting the most frequent result.
Best-of-N: generate several candidates and use a selection rule to choose one.
Self-refinement: give the model an opportunity to revise an earlier response.
These approaches have different mechanics, but they share a budget consequence: additional candidates, longer outputs or revision rounds consume more generation work. A better answer can therefore come with higher latency and lower throughput.
The repository’s MATH-500 materials connect these techniques to evaluation, including verifier scripts and an advanced parser. That detail is important. Scoring mathematical answers involves extracting and interpreting the answer, not simply checking whether two output strings look identical.
My recommendation would be to compare each method against the same baseline questions and scoring rules, while also recording how much extra generation it requires. An accuracy increase and an efficiency improvement are different results. A method can deliver one without delivering the other.
There is also a practical boundary between evaluation and deployment. A benchmark verifier that checks against a known reference answer can score an experiment. It cannot automatically become a production answer selector when the correct answer is unknown. Best-of-N needs a usable selection signal, not merely more candidates.
That is one of the course’s most useful lessons: additional computation is an intervention to measure, not a guarantee of better reasoning.
Reinforcement Learning and Distillation Change the Weights
Chapters 6–8 move from changing the generation process to changing the model through training.
Raschka’s announcement highlights reinforcement learning with verifiable rewards. In a mathematical task, for example, a checking procedure can determine whether the final answer is correct and provide a reward signal. Training then uses that signal to adjust the probability of generated behavior.
GRPO, short for Group Relative Policy Optimization, uses groups of responses to help determine which outputs should be encouraged relative to others. At a conceptual level, the group supplies a comparison signal: responses receiving better rewards are favored relative to lower-reward responses. The course moves from implementing reinforcement learning to examining improvements to GRPO in a separate chapter.
The reward definition deserves close attention. A training loop can optimize the signal it receives, but that signal must capture something useful. Answer extraction errors, weak checking rules or rewards tied too closely to formatting can distort what the experiment measures.
Distillation takes a different route. A student model learns from outputs produced by another model, transferring useful behavior through training examples. The repository frames chapter 8 around efficient reasoning.
The practical tradeoff is where the work happens. Inference-time scaling spends additional compute when answering a question. Distillation shifts some work into producing training data and updating the student, with the aim of making the resulting model more useful or efficient afterward.
I find that division more informative than treating all four topics as interchangeable “reasoning upgrades.” They change different parts of the system, carry different costs and need different measurements.
The repository describes its target as a small, functional reasoning model for educational purposes. That scope supports learning how these methods work; it is not evidence that completing the notebooks produces frontier-level performance.
Prerequisites and a Realistic Compute Plan
Raschka recommends being comfortable with Python and PyTorch. For a coding-heavy course, that means being able to follow tensor operations, model execution and training code without having to learn every language feature along the way.
The hardware guidance supports a staged approach:
Workload
Published guidance
Practical planning implication
Chapters 2–4: generation, evaluation and initial inference scaling
The repository says these work well on CPUs and GPUs.
Start locally before committing to GPU resources.
Chapters 5–6: self-refinement and reinforcement learning
The repository recommends a GPU to replicate the chapter results.
Plan GPU access before attempting the fuller experiments.
Later training experiments
The announcement says a GPU makes training much more practical.
Budget for training separately from watching videos or inspecting notebooks.
The repository says the main-chapter code is designed largely for consumer hardware and automatically uses GPUs when available. These are the author’s hardware recommendations; the supplied documentation does not establish a universal VRAM minimum, training price or completion time.
A small model reduces the barrier, but it does not make every workload equally cheap. Sampling several answers increases generation work. Training adds memory requirements for gradients and optimizer state, and longer sequences increase the amount of computation. The relevant budget is the experiment you intend to run, not simply whether the model can load.
Start With Generation and a Baseline
The repository provides this command for downloading a shallow copy of the code:
The command downloads the repository and its latest working files without the full commit history. It does not install dependencies or configure a Python environment. Chapter 2 contains Python and environment setup guidance, and the repository includes a troubleshooting guide.
A practical starting route is:
Read chapter 1 to establish the scope.
Follow chapter 2’s setup guidance and run text generation.
Use chapter 3 to establish an evaluation baseline.
Compare inference-time techniques before allocating resources to training.
Move into reinforcement learning and distillation with the evaluation pipeline already working.
The optional materials include Qwen3 implementation details, larger-model examples, batching and throughput-oriented execution, evaluation approaches and a chat interface. Those offer follow-up paths after the main sequence without making them prerequisites.
For developers building AI products, the immediate value is an inspectable chain from generation to measurement to improvement. A useful first milestone is a model that runs, an evaluation process you understand and a baseline against which the next technique can be judged.
Frequently Asked Questions
3 questions
1
Does “From Scratch” Include LLM Pretraining?
No. The course starts with a pretrained Qwen3 model and implements the reasoning and evaluation methods around it.
2
Is Raschka’s Earlier LLM Course Required?
No. Raschka recommends familiarity with Python and PyTorch, but says the reasoning materials are standalone.
3
Does the Course Focus on Building AI Agents?
Its focus is the reasoning model. Raschka distinguishes that model from an agent framework, which adds tools and context management around it.
