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cs.LG2026
UniComp: A Unified Evaluation of Large Language Model Compression via Pruning, Quantization, and Distillation
Jonathan von Rad, Yong Cao, Andreas Geiger
Model compression is increasingly essential for deploying large language models (LLMs), yet existing comparative studies largely focus on pruning and quantization evaluated primari…
cs.LG2025
MDPO: Overcoming the Training-Inference Divide of Masked Diffusion Language Models
Haoyu He, Katrin Renz, Yong Cao +1
Diffusion language models, as a promising alternative to traditional autoregressive (AR) models, enable faster generation and richer conditioning on bidirectional context. However,…