arXiv:2602.20062v2 Announce Type: replace Abstract: Pretraining and fine-tuning are central stages in modern machine learning systems. In practice, feature learning plays an important role across both stages: deep neural networks learn a broad range of useful features during pretraining and further refine those features during fine-tuning. However, an end-to-end theoretical understanding of how choices of initialization impact the ability to reuse and refine features during fine-tuning has remained elusive. Here we develop an analytical theory of the pretraining fine-tuning pipeline in diagona
The rapid advancement and widespread adoption of large foundation models necessitate a deeper theoretical understanding of their underlying mechanisms to improve efficiency and reliability.
A comprehensive theory of pretraining's inductive bias on fine-tuning will allow for more principled model design, reducing trial-and-error and accelerating AI development, impacting overall compute efficiency.
The development pathway for complex AI models will shift from empirical experimentation to more theoretically guided engineering, potentially democratizing access to high-performance AI.
- · AI researchers and developers
- · Cloud AI providers
- · Startups with limited compute budgets
- · Organizations relying solely on brute-force compute for model development
Improved understanding of how pretraining affects fine-tuning performance and efficiency.
More efficient and targeted use of computational resources for AI model development and deployment across various industries.
Reduced resource barriers to entry for developing advanced AI, potentially leading to a more diverse and competitive AI ecosystem.
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Read at arXiv cs.LG