AI RESEARCH
BoHA: Blockwise Hadamard Product Adaptation for Parameter-Efficient Fine-Tuning
arXiv CS.LG
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ArXi:2509.21637v2 Announce Type: replace Parameter-efficient fine-tuning (PEFT) of large language models trains a small task-specific parameter set while keeping the pretrained model frozen. The dominant Low-Rank Adaptation (LoRA) family makes this trade-off practical; however, evaluations under the same parameter budget assess single-task accuracy. In sequential adaptation settings, such evaluations should also measure how well performance on the first-stage task is retained after subsequent fine-tuning. To address this gap, we.