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Research

Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions

Polynomial expansion techniques boost the efficiency of low-rank model fine-tuning by leveraging high-order interactions, advancing parameter-efficient adaptation methods for large-scale models.

Wednesday, April 15, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline

Research paper presenting Polynomial Expansion Rank Adaptation, a technique enhancing low-rank fine-tuning through high-order polynomial interactions. Contributes to parameter-efficient model adaptation methods.

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