arXiv:2602.12952v2 Announce Type: replace Abstract: Adapting large pre-trained models to downstream tasks often produces task-specific parameter updates that are expensive to relearn for every model variant. While recent work has shown that such updates can be transferred between models with identical architectures, transferring them across models of different widths remains unexplored. In this work, we introduce Theseus, a training-free method for transporting task updates across heterogeneous-width models. Rather than matching parameters, we characterize a task update by the functional effec
The rapid development and deployment of large language models create an urgent need for efficient adaptation and transfer of learned capabilities across diverse model architectures without expensive retraining.
This development significantly enhances the flexibility and efficiency of deploying AI models, potentially reducing the computational and financial costs associated with adapting pre-trained models.
The ability to transport task updates across different model widths without training means AI models can be more easily optimized or scaled to various hardware constraints or performance requirements.
- · AI developers
- · Cloud computing providers (reduced egress/ingress for model fine-tuning)
- · Companies with diverse AI deployment needs
- · Hardware manufacturers (more efficient use of varied AI accelerators)
- · Traditional fine-tuning services
- · Anyone relying on architecture-specific optimizations
Reduced computational overhead and time for adapting large AI models to new tasks or hardware.
Accelerated iteration cycles for AI development and deployment, leading to faster innovation in applied AI.
Enhanced accessibility for smaller organizations to leverage advanced AI models by making adaptation less resource-intensive.
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Read at arXiv cs.LG