AI RESEARCH
PiCa: Parameter-Efficient Fine-Tuning with Column Space Projection
arXiv CS.LG
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ArXi:2505.20211v3 Announce Type: replace Fine-tuning large foundation models is essential for building expert models tailored to specialized tasks and domains, but fully updating billions of parameters is computationally prohibitive. Reducing the number of trainable parameters using Parameter-Efficient Fine-Tuning (PEFT), such as Low-Rank Adaptation (LoRA), is. therefore. crucial not only to reduce