arXiv:2602.07488v3 Announce Type: replace-cross Abstract: Despite the fact that experimental neural scaling laws have substantially guided empirical progress in large-scale machine learning, no existing theory can quantitatively predict the exponents of these important laws for any modern LLM trained on any natural language dataset. We provide the first such theory in the case of data-limited scaling laws. We isolate two key statistical properties of language that alone can predict neural scaling exponents: (i) the decay of pairwise token correlations with time separation between token pairs,
This research provides a foundational theoretical understanding of neural scaling laws, which have primarily been empirical until now, suggesting a maturation in AI research. Published on arXiv, it signifies early but impactful work in the academic community.
Understanding the theoretical underpinnings of neural scaling laws allows for more deliberate and efficient LLM design, potentially reducing the empirical trial-and-error often associated with large-scale AI development. This can accelerate progress and resource optimization in AI research and deployment.
The ability to quantitatively predict neural scaling exponents for LLMs based on statistical properties of language fundamentally shifts the approach from experimental observation to theoretical prediction in AI model design. This could lead to more predictable and cost-effective development.
- · AI researchers
- · LLM developers
- · Cloud compute providers
- · Organizations relying solely on empirical scaling strategies
- · Inefficient AI development paradigms
More efficient and predictable development of large language models, leading to faster innovation cycles.
Reduced computational costs for achieving specific performance benchmarks in AI models, making advanced AI more accessible.
Acclerated adoption of AI across various sectors due to lower development barriers and more reliable performance predictions, potentially impacting economic productivity globally.
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Read at arXiv cs.AI