CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging 事件

CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging arXiv:2603.00573v2 Announce Type: replace Abstract: Large language models (LLMs) achieve remarkable performance on diverse downstream and domain-specific tasks via parameter-efficient fine-tuning (PEFT). However, existing PEFT methods, particularly MoE-LoRA architectures, suffer from limited parameter efficiency and coarse-grained adaptation due to the proliferation of LoRA experts and instance-level routing. To address the