GPart: End-to-End Isometric Fine-Tuning via Global Parameter Partitioning
Low-rank adaptation (LoRA) has become a dominant paradigm for parameter-efficient fine-tuning (PEFT) of large-scale deep learning models. However, its bilinear parameterization induces a parameter-dependent geometry: the mapping from trainable parameters to weight updates is not generally distance-preserving. Related methods that project a low-dimensional vector into LoRA's parameter space, such as Uni-LoRA, improve parameter efficiency, but the subsequent bilinear map breaks end-to-end isometry. We propose GPart (Global Partition fine-tuning), a highly parameter-efficient fine-tuning method that maps a $d$-dimensional trainable vector directly into the full weight space through a sparse,...
| Authors | Paolo Mandica, Michał Brzozowski, Zuzanna Dubanowska et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 May 2026 |
| Last updated at source | 2 October 2026 |
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