What Makes a Strong Model? A Unified Spectral Analysis of Knowledge Transfer over High-dimensional Linear Regression
arXiv:2606.01292v1 Announce Type: new Abstract: Teacher-Student Knowledge Transfer (KT) is ubiquitous in modern machine learning, ranging from classical model compression via Knowledge Distillation (KD) to the emergent phenomenon of Weak-to-Strong (W2S) generalization. While existing studies offer isolated insights, a unified theoretical framework explaining the efficacy of KT across these disparate regimes remains lacking. In this work, we establish a unified spectral analysis of SGD dynamics in high-dimensional linear regression, elucidating the efficiency of KT across seemingly disparate re
The proliferation of complex AI models and the increasing need for efficient knowledge transfer methods make unified theoretical frameworks like this particularly relevant.
A unified spectral analysis of knowledge transfer could lead to more efficient and robust AI model development, impacting capabilities from model compression to advanced generalization.
This research provides a deeper theoretical understanding of how knowledge transfer works in AI, potentially guiding future architectural choices and training methodologies.
- · AI researchers
- · Machine learning platforms
- · AI model developers
- · Developers relying on ad-hoc knowledge transfer methods
Improved understanding of AI model efficiency and generalization.
Development of new knowledge transfer techniques based on spectral analysis.
More resource-efficient and adaptable AI systems in various applications.
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Read at arXiv cs.LG