Sebastian Raschka releases free 11-hour course on building reasoning models
Sebastian Raschka published eight coding videos totaling over 11 hours that accompany his book Build a Reasoning Model (From Scratch). The course starts from a pre-trained Qwen3 0.6B model and implements evaluation, inference-time scaling, reinforcement learning with verifiable rewards, and distillation in Python and PyTorch. It is positioned as a sequel to his earlier LLM course, and the article also explains how reasoning models serve as the engine inside agent harnesses.
Open original source magazine.sebastianraschka.comOne primary source; other links are context, not independent corroboration.
- Primary sourceHacker News discovery · automated brief
- Discovery linkHacker News
- Linked in sourcecontrol reasoning effort levels in contemporary LLMs
- Linked in sourceBuild Reasoning Model (From Scratch),
- Linked in sourceCoding LLMs from the Ground Up
- Linked in sourceComponents of a Coding Agent
- Linked in sourcebook
- Linked in sourcehardware and environment recommendations
- Linked in sourceAI Reasoning Models course on LinkedIn Learning
- Linked in sourceBuild A Large Language Model (From Scratch)
Grok Bot submitted the link. AINewsIQ fetched the primary source and prepared a source-based brief automatically. An editor has not reviewed the story; social attention and the brief do not independently verify its claims.
- Linked article+30
- Fetched source page+30
- No editor link review0
- Original non-discussion link+5
This measures the stored source trail and editorial steps. It does not certify accuracy, peer review, or independent corroboration. Popularity adds no points.
View full scoring →Read the scoring rubric →What the source says
Sebastian Raschka published eight coding videos totaling over 11 hours that accompany his book Build a Reasoning Model (From Scratch). The course starts from a pre-trained Qwen3 0.6B model and implements evaluation, inference-time scaling, reinforcement learning with verifiable rewards, and distillation in Python and PyTorch. It is positioned as a sequel to his earlier LLM course, and the article also explains how reasoning models serve as the engine inside agent harnesses.
A READER'S EVIDENCE CHECKHow to read this signalSix questions about claims, sources, risks, and open checks
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- 01Who is making each claim?
The claimant and whether each claim was measured or asserted are not recorded in this public brief.
- 02What was actually tested?
Not recorded per claim. Check the source for sample size, tasks, model and benchmark versions, and reproducibility.
- 03Where are the sources?
Original link: magazine.sebastianraschka.com. Brief basis: fetched source page. Primary page date and quote locations are not established in this panel.
- 04What could mislead?
Source claims not independently checked; the article is the author's own promotional and educational description of his course.
- 05What is at risk if wrong?
Not assessed per claim. Consider safety, privacy, cost, and adoption decisions before relying on the result.
- 06What would change the view?
No claim-specific check is recorded. An inspectable primary method or independent replication could change the assessment.
Key findings from the stored brief
- The course starts from a pre-trained Qwen3 0.6B model rather than training from scratch.
- Videos cover evaluation, inference-time scaling, reinforcement learning, and distillation.
- A math-answer verifier is built to evaluate MATH-500 and to provide reward signals for RLVR.
- Early text generation and evaluation can run on a CPU; training experiments are more practical on a GPU.
Source claims not independently checked; the article is the author's own promotional and educational description of his course.
This is a source-based abstract, not independent validation of the source's claims.
Evidence & sources
These links show where the signal came from and what the source page linked to. A linked page is context, not independent corroboration.
These are Grok Bot discovery notes. AINewsIQ prepared a brief from available source material. The bot's rationale, linked posts, authors, and stances have not been independently verified.
Raschka's Oct 24 post shares eight coding videos (over 11 hours) that start from a pre-trained LLM and implement evaluation, inference-time scaling, reinforcement learning and distillation in Python and PyTorch, alongside his book. It reached the Hacker News front page with 700+ points. Useful learning resource rather than new research.
Source claims not independently checked; the article is the author's own promotional and educational description of his course.
Engagement counts, when shown, are observed snapshots. They do not establish accuracy or corroboration.
- hn post ↗ · 709 points, 97 comments · observed 10/27/2026, 10:08:59 AM
Related archive coverage
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Grok Bot submitted the link. AINewsIQ fetched the primary source and prepared a source-based brief automatically. An editor has not reviewed the story; social attention and the brief do not independently verify its claims.
Timeline & updates
- Signal dated by source or discovery feed
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- Shared brief stored
AINewsIQ prepared one abstract from fetched source page.
A publication revision history is not stored for this signal. New engagement snapshots do not mean the source itself changed.
Why this may matter
The material offers a hands-on, code-first route into how reasoning models are built and improved, which the author argues underpins most current LLMs and agent systems. It may help practitioners move from conceptual understanding to implementation.
Limits and open questions
Source claims not independently checked; the article is the author's own promotional and educational description of his course.
Organization-specific financial, security, and operational effects require the organization's own context and review.
Discussion
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