Gyeongman Kim
1 indexed paper
Recent (6 mo)
1With code
0Influential cites
0Benchmarked
0Publications per year
126
Top categories
NLP×1AI×1ML×1
Frequent co-authors
Research Timeline
2026
Pruning and Distilling Mixture-of-Experts into Dense Language Models
The paper introduces a systematic framework to convert large Mixture-of-Experts (MoE) models into memory-efficient, fully dense architectures, achieving superior performance compared to traditional pruning methods.
Highlighted terms show continued research focus across papers
Papers
cs.CLcs.AIcs.LGRecentMay 27, 2026
Pruning and Distilling Mixture-of-Experts into Dense Language Models
Junhyuck Kim, Jihun Yun, Haechan Kim, Gyeongman Kim +2 more
The paper introduces a systematic framework to convert large Mixture-of-Experts (MoE) models into memory-efficient, fully dense architectures, achieving superior performance compared to traditional pr…
View →