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 these issues, we propose Core Space Mixture of LoRA (\textbf{CoMoL}), a novel MoE-LoRA framework that incorporates expert diversity, parameter efficiency, and fine-grained adap
Rapid advancements in LLM architectures are constantly pushing for greater efficiency and performance, and this research addresses current limitations in LoRA expert models.
Improving the parameter efficiency and fine-grained adaptation of large language models directly impacts their deployment costs and capabilities across various specialized tasks.
The proposed CoMoL method suggests a more efficient way to utilize Mixture of Experts (MoE) architectures, potentially leading to more scalable and adaptable PEFT for LLMs.
- · AI developers
- · Cloud providers
- · Businesses adopting LLMs
- · Less efficient PEFT methods
- · Non-optimized model deployment strategies
More widespread and cost-effective deployment of specialized large language models.
Increased accessibility of advanced AI capabilities to a broader range of enterprises and applications.
Acceleration of AI agent development due to more efficient and adaptable underlying models.
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