LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing
Mixture-of-Experts (MoE) based Large Language Models (LLMs) have demonstrated impressive performance and computational efficiency. However, their deployment is often constrained by substantial memory demands, primarily due to the need to load numerous expert modules. While existing expert compression techniques like pruning or merging attempt to mitigate this, they often suffer from irreversible knowledge loss or high training overhead. In this paper, we propose a novel expert compression paradigm termed expert replacing, which replaces redundant experts with parameter-efficient modules and recovers their capabilities with low training costs. We find that even a straightforward baseline o...
| Authors | Jiawei Hao, Zhiwei Hao, Jianyuan Guo et al. |
|---|---|
| Primary category | cs.LG |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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