arXiv:2606.02008v1 Announce Type: cross Abstract: Pre-training has become a fundamental paradigm in modern machine learning, with one of its key empirical benefits being reduced downstream sample complexity as the scale of pre-training data increases. However, existing theoretical frameworks for pre-training do not fully explain this phenomenon. In this paper, we introduce complexity minimization, a novel meta-representation learning framework designed to enable theoretical analysis of this scaling behavior, which learns representations by evaluating the downstream model complexity best suited
This research provides a theoretical framework to explain and potentially optimize the empirical benefits of pre-training, which is a core paradigm in current AI development.
A provable data scaling law allows for more efficient and predictable scaling of AI models, directly impacting the development costs and performance of future AI systems.
The ability to theoretically analyze and predict the scaling behavior of meta-learning through complexity minimization offers a clearer path to optimizing data use in pre-training.
- · Large language model developers
- · Meta-learning researchers
- · AI compute infrastructure providers
More efficient pre-training leads to faster development cycles for AI models.
Improved theoretical understanding could reduce the need for purely empirical, trial-and-error scaling methods.
This could accelerate the development of more general and less data-hungry AI systems.
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Read at arXiv cs.LG