arXiv:2605.20196v1 Announce Type: cross Abstract: 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. We work with a suffix-automaton representation of text corpora and define a data-intrinsic global-KL predictive contribution spectrum, in which each state contributes according to its empirical mass times its KL deviation from a global next-token baseline. Across 12 real corpora, the tail slope of this spectrum is already strongly correlated with the empirical dat
This paper offers a novel theoretical framework for understanding data scaling laws in AI, moving beyond traditional explanations to focus on the 'predictive contribution spectrum.' This timing is critical as the industry grapples with the diminishing returns and increasing costs of simply scaling models with more data.
A strategic reader should care because a deeper understanding of how data contributes to predictive power can lead to more efficient and targeted data curation, potentially altering the economics and technical trajectory of large model development. This could fundamentally change how 'data quality' is defined and optimized.
The explicit recognition and quantification of a data-intrinsic 'predictive contribution spectrum' shifts the focus from mere data quantity to the quality and informational density of data points, suggesting new metrics for data value. This could lead to a re-evaluation of data acquisition strategies.
- · AI research labs
- · Data science platforms
- · Companies with proprietary, information-rich datasets
- · Software companies specializing in data instrumentation
- · Companies relying on undifferentiated, large-scale public data scraping
- · Those without sophisticated data analysis capabilities
- · Traditional data brokers focused purely on volume
AI models will likely become more data-efficient, potentially requiring less raw data to achieve similar or superior performance.
This efficiency could reduce computational costs and environmental impact, as less redundant data processing would be needed.
A refined understanding of data's predictive value might foster the development of 'synthetic' or expertly-curated datasets, challenging the dominance of raw, scraped internet data for AI training.
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Read at arXiv cs.LG