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Arxiv
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LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning
- LoFT is a new low-rank adaptation method that aligns optimizer dynamics with full fine-tuning.
- It behaves like full fine-tuning by learning weight updates in a low-rank subspace and projecting optimizer's moments.
- LoFT eliminates the need for tuning extra hyperparameters and narrows the performance gap between adapter-based tuning and full fine-tuning.
- Empirically, LoFT outperforms standard LoRA-style methods without increasing inference cost.
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