arXiv:2603.07523v2 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales. In response to this challenge, recent approaches typically resort to either parameter selection, which fails to capture the interdependent structure of this knowledge, or parameter prediction using generative models that depend on impractical access to large network col
The proliferation of various large-scale pre-trained models necessitates more flexible and efficient knowledge transfer methods beyond monolithic fine-tuning, driving research into architecture-agnostic initialization.
This research addresses a fundamental limitation in current AI development by allowing more flexible reuse of pre-trained knowledge across models of varying scales, potentially accelerating innovation and reducing computational costs.
The ability to transfer knowledge more effectively across different model architectures could lead to a decoupling of pre-trained knowledge from specific monolithic models, fostering more adaptable and efficient AI development.
- · AI researchers and developers
- · Companies using diverse AI model architectures
- · Cloud AI providers
- · Developers solely reliant on rigid fine-tuning paradigms
- · Companies with highly specialized, non-transferable AI models
More efficient and versatile deployment of AI models for downstream tasks, reducing the need for extensive re-training.
Accelerated development cycles for new AI applications as knowledge transfer becomes less architecture-dependent.
Lower barriers to entry for developing competitive AI models, potentially increasing market competition and democratizing advanced AI capabilities.
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Read at arXiv cs.LG