2 papers
cs.LG2026
ACE: Adapter Consolidation across Experts for Parameter-Efficient Fine-Tuning of MoE LLMs
Ahin Lee, Sehyun Yun, Joonha Park +1
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…
cs.LG2026
EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning
Ahin Lee, Sehyun Yun, Taesik Gong
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 (PE…