Is LLM Pruning a Lie? Training Small Models from Scratch May Be Better
burny_tech · x · 2026-07-31
A technical discussion on the effectiveness of structural pruning in Large Language Models (LLMs).
The author argues that if sufficient training tokens are available, training a smaller dense LLM from scratch can match or even outperform a structurally pruned large model.
He suggests that coarse depth or width pruning doesn't actually transfer knowledge, but rather acts as an expensive form of Neural Architecture Search (NAS).
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