GPart: Isometric Fine-Tuning via Global Parameter Partitioning
Introducing GPart (Global Partition fine-tuning), a novel method for parameter-efficient fine-tuning that completely eliminates the low-rank limitation. In contrast to LoRA and its derivatives, GPart employs a singular isometric partition matrix to directly translate a d-dimensional trainable vector into the model's complete weight space. This approach maintains the distance within the optimization landscape, effectively addressing the fundamental distortion issue. The process is streamlined to a remarkably simple pipeline: a single random projection, executed end-to-end. The research can be found on arXiv with the identifier 2605.14841.
Key facts
- GPart removes the low-rank bottleneck entirely.
- Uses a single isometric partition matrix.
- Maps a d-dimensional trainable vector directly into full weight space.
- Preserves distance in the optimization landscape.
- Results in an extremely minimal fine-tuning pipeline.
- Paper available on arXiv: 2605.14841.
- Addresses limitations of LoRA and Uni-LoRA.
- Method is end-to-end isometric.
Entities
Institutions
- arXiv