arXiv:2605.29280v1 Announce Type: new Abstract: Knowledge distillation (KD) transfers a single scalar prediction from a large foundation model (FM) to compact vertical models (VMs), suffering from diminishing transfer ratio -- the fraction of FM improvement captured by the VM -- as a single scalar cannot convey the rich intermediate knowledge that larger FMs learn. To address this bottleneck, we propose LoopFM (Learning frOm HistOrical ReP*resentations of FM), a framework that opens a high-bandwidth transfer channel by structuring FM intermediate embeddings as input features (e.g., user histor
The increasing scale and complexity of foundation models necessitate more efficient knowledge transfer methods as their real-world deployment becomes ubiquitous.
This breakthrough improves the efficiency of deploying large foundation models into compact vertical models, making advanced AI capabilities more accessible and performant for specialized tasks.
Knowledge transfer from large foundation models can now leverage rich intermediate embeddings instead of just scalar predictions, significantly improving the performance of smaller derived models.
- · AI model developers
- · Cloud computing providers
- · Companies deploying specialized AI
- · Legacy knowledge distillation techniques
More powerful and efficient specialized AI applications will emerge across various industries.
Reduced computational costs for deploying advanced AI could accelerate AI adoption in resource-constrained environments.
This could lead to a proliferation of highly capable, fine-tuned AI agents and services, impacting how businesses operate.
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Read at arXiv cs.LG