On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry
arXiv:2603.27631v2 Announce Type: replace Abstract: Self-supervised pre-training, where large corpora of unlabeled data are used to learn representations for downstream fine-tuning, has become a cornerstone of modern machine learning. While a growing body of theoretical work has begun to analyze this paradigm, existing bounds leave open the question of how sharp the current rates are, and whether they accurately capture the complex interaction between pre-training and fine-tuning. In this paper, we address this gap by developing an asymptotic theory of pre-training via two-stage M-estimation.
The proliferation of self-supervised pre-training in AI models necessitates a deeper theoretical understanding of its underlying mechanisms and performance boundaries.
This research provides critical theoretical foundations for optimizing AI model development, leading to more efficient and powerful machine learning systems.
Our understanding of how pre-training and fine-tuning interact is now more robust, potentially guiding future architectural and training methodology advances.
- · AI researchers
- · Machine learning developers
- · Large language model companies
- · AI models without rigorous theoretical backing
- · Inefficient AI training approaches
Improved pre-training techniques will lead to more effective and generalizable AI representations.
The cost and computational resources required for developing high-performing AI models could potentially decrease due to optimized pre-training.
Enhanced theoretical understanding of AI could accelerate breakthroughs in various scientific and industrial applications by making AI development more predictable.
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Read at arXiv cs.LG