arXiv:2607.00162v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) reparameterizes weight updates in a fixed basis: low-rank adapters operate in the spatial domain, while a recent line of spectral methods operates in a fixed Fourier domain. We argue that the choice of domain is itself a design degree of freedom that should be learned, and that no single basis is optimal across tasks, layers, or tokens. We introduce Fractional-Fourier Mixture of Experts, a mixture-of-experts adapter in which every expert carries a learnable fractional-Fourier order that continuously interpol
The continuous improvement of parameter-efficient fine-tuning (PEFT) methods is crucial for making large AI models more adaptable and accessible, leading to a new approach that learns the optimal adaptation domain itself.
This research suggests a more efficient and flexible approach to fine-tuning large language models, potentially significantly reducing computational costs and improving performance across diverse tasks and architectures.
Instead of fixed adaptation domains (spatial or Fourier), the proposed Fractional-Fourier Mixture of Experts allows AI models to learn the most suitable domain for adaptation, leading to more generalized and efficient fine-tuning.
- · AI researchers
- · Developers of large language models
- · Companies with diverse AI deployment needs
- · Edge AI applications
- · Fixed-domain PEFT approaches
- · Compute-intensive fine-tuning methods
Increased efficiency and performance of parameter-efficient fine-tuning for large AI models.
Broader adoption of sophisticated fine-tuning techniques, enabling more rapid customization and deployment of AI.
Democratization of advanced AI capabilities by lowering computational barriers for adaptation.
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Read at arXiv cs.LG