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ACE: Adapter Consolidation across Experts for Parameter-Efficient Fine-Tuning of MoE LLMs
Parameter-efficient fine-tuning (PEFT) of mixture-of-experts (MoE) models commonly attaches a separate low-rank adapter to each expert. This expert-wise design fragments adaptation in three ways: capacity is split across narrow low-rank updates, gradient supervision becomes sparse and imbalanced und
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