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cs.LG2026
Layer-wise Curriculum Learning for Efficient LLM Compression
Donggeon Lee, Dooyeon Na, Seungmin Oh +1
In this paper, we introduce layer-wise curriculum learning for efficient LLM compression. The proposed method facilitates the knowledge transfer from the teacher model to the stude…
cs.LG2026
Colla-Q: Toward Collaborative Experts in MoE Quantization via Minimax Precision Balancing
Eunju Shin, Jongbin Ryu
In this paper, we present a Mixture-of-Experts (MoE) quantization method based on activation entropy. Although quantization reduces memory and computational costs, it can substanti…