arXiv:2605.20545v1 Announce Type: cross Abstract: Transfer learning is an essential technique for many machine learning/AI models of complex structures such as large language models and generative AI. The essence of transfer learning is to leverage knowledge from resolved source tasks for a new target task, especially when the sample size $m$ of the training data for the latter is low. In this work, we rigorously analyze the potential benefit of transfer learning in terms of sample efficiency. Specifically, taking an optimal transport viewpoint of transfer learning, we find that when the data
This research provides a theoretical advancement in optimizing transfer learning, a critical technique for improving the efficiency and robustness of AI models, particularly large language models and generative AI.
Understanding the sample complexity of transfer learning can lead to more efficient and less data-intensive AI development, reducing the computational and data annotation burdens previously associated with advanced AI systems.
New methodologies for transfer learning, potentially reducing the sample size required for effective model training, which could accelerate AI deployment in resource-constrained environments.
- · AI compute providers
- · Machine learning researchers
- · Generative AI developers
- · Companies with limited data for specific tasks
- · Companies reliant on brute-force data collection
- · Inefficient AI training methodologies
Improved efficiency in training and deploying large AI models.
Faster development cycles for new AI applications and potentially lower barriers to entry for AI innovation.
More ubiquitous and specialized AI models across various sectors due to reduced resource requirements.
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Read at arXiv cs.LG