Study on data scaling laws for LLMs identifies new pattern
A new preprint study published on arXiv suggests that data scaling laws for large language models are governed by a spectrum of predictive contribution, rather than solely token frequency. The research analyses suffix automata across twelve text corpora.
What happened?
Researchers have published a preprint study (arXiv:2605.20196v1) exploring the hypothesis that data scaling laws for LLMs are driven by progressive coverage of a latent spectrum of predictive contribution. This contrasts with the previously dominant understanding that only token frequency tails are decisive. The study applies a suffix automaton representation of text corpora.
Key facts
| Publikationsdatum | 26 maj 2026 |
|---|---|
| arXiv ID | 2605.20196v1 |
| Antal korpusar analyserade | 12 |
| R² för rått spektrum (log K vs log N) | 0.96 |
| R² för utjämnat spektrum (log K vs log N) | 0.90 |
”We investigate the hypothesis that real-data scaling laws are governed by progressive coverage of a latent predictive contribution spectrum rather than by token-frequency tails alone.”
Why it matters
The study's findings contribute to a deeper understanding of how data affects the performance of large language models during training. Identifying a "predictive contribution spectrum" could lead to more efficient data selection and training methods, which may optimise model learning and reduce training costs. This has implications for the development of more capable and resource-efficient AI systems.
Who is affected?
This study is primarily aimed at AI researchers, machine learning engineers, and developers of large language models working on data training and model optimisation. Companies developing or using LLMs are indirectly affected, as are end-users of AI applications through potentially more efficient and superior models.
What else you should know
The study utilises a "global-KL predictive contribution spectrum" where each state contributes based on its empirical mass multiplied by its KL divergence from a global next-token baseline. The correlation was observed across twelve different text corpora.
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