arXiv:2607.01789v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models scale efficiently but remain costly to adapt due to redundant experts and uniform parameter allocation. Existing parameter-efficient fine-tuning (PEFT) methods such as LoRA ignore MoE routing dynamics, leading to suboptimal resource use. We propose EPnG, an adaptive prune-and-grow framework that reallocates LoRA capacity based on expert importance derived from router gate probabilities. EPnG prunes under-utilized experts and expands high-importance experts via rank growth with orthogonal initialization, while maint
The increasing scale and computational cost of Mixture-of-Experts (MoE) models necessitate more efficient fine-tuning methods to make them practical for broader applications, driving innovation in dynamic parameter allocation.
This development improves MoE model adaptability and efficiency, potentially broadening their accessibility and reducing the computational resources required for advanced AI model deployment and fine-tuning, impacting the scalability of large language models.
MoE fine-tuning moves beyond uniform parameter allocation, adopting a more dynamic, importance-based pruning and growth strategy that can lead to more efficient and adaptable AI models.
- · AI developers
- · Cloud providers (via optimized resource use)
- · Organizations deploying large language models
- · Hardware manufacturers (via more efficient model utilization)
- · Inefficient AI model fine-tuning techniques
- · Organizations with limited compute resources (if they don't adopt such methods)
More widespread and cost-effective deployment of large, specialized AI models becomes feasible.
Increased competition in AI model development as the barrier to entry for fine-tuning state-of-the-art models is lowered.
Acceleration of personalized AI applications and agentic systems due to enhanced adaptability and lower operational costs of MoE models.
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Read at arXiv cs.LG