6 Things I Learned Building LLMs From Scratch That No Tutorial Teaches You

Towards Data Science / 4/17/2026

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Key Points

The article explains several practical optimizations behind modern Transformer-based LLMs, focusing on stability and performance rather than basic “how-to” steps.
It highlights rank-stabilized scaling as a technique to improve training behavior and scaling characteristics.
It discusses quantization stability, emphasizing how numerical compression can be managed without degrading model reliability.
The piece frames these techniques as statistical and architectural design choices that help make LLM training and deployment more robust.

From rank-stabilized scaling to quantization stability: A statistical and architectural deep dive into the optimizations powering modern Transformers.

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