AI

Build Your Own Reasoning Model: A Full Video Course

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 24/10/2026, 12:21:24 UTC.

Briefings

The author released eight video lectures and a book on building reasoning models from scratch using PyTorch, starting with a pre-trained Qwen3 model. These resources cover inference scaling, reinforcement learning, and distillation, with the first video focusing on environment setup. The content is presented as a sequel to a previous course on LLMs from the ground up.

Key points

• The author released eight video lectures and a book on building reasoning models from scratch using PyTorch, starting with a pre-trained Qwen3 model.
• The first video focuses on environment setup and introduces the relationship between conventional LLMs, reasoning models, and agents.

Context

The author previously published a book and video course on building LLMs from scratch. This new series continues that work by implementing reasoning methods in PyTorch, focusing on evaluation, scaling, and RL for a small Qwen3 model.

Limitations

The evidence is limited to the author's own video lectures and book content. No external data, user feedback, or performance metrics are provided. The specific hardware requirements for early CPU work versus GPU training are mentioned but not detailed.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 24/10/2026, 12:46:09 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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