Data-constrained scaling laws for underparameterized LLMs
The author argues that LLMs are heavily underparameterized relative to the brain's synapse count and token exposure, and proposes data-constrained scaling laws for very large transformers.
By Wendelmaques Pereira · Updated today
Source: Leis de escala com restrição de dados para LLMs subparametrizados (x.com). Text prepared with AI from this source.
What happened and what to do
The post claims that language models are severely underparameterized when compared with the number of synapses in the brain and with the number of tokens they are exposed to. The author presents data-constrained scaling laws and learning algorithms aimed at very large transformers, questions the optimal-scaling assumptions of the Chinchilla type, and proposes scaling parameters and data in a different way.
For a company that trains or fine-tunes models, this discussion affects how compute budgets are planned: how many parameters to use and how much useful, high-quality data is actually available. In practice, it is worth building a pipeline that measures the supply of usable data per domain, records loss curves across different model sizes, and compares scaling regimes in controlled experiments before committing to large training runs. The claims of a post do not replace tests on your own dataset and your own budget.
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