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cs.LG2026
MoPLEx: Estimating Plackett-Luce Mixture Models for Multi-Objective Alignment
Dongyue Li, Ziniu Zhang, Lu Wang +1
We study learning a mixture of Plackett-Luce models from multi-way ranking responses from annotators that may represent heterogeneous underlying preferences. This problem has m…
cs.CL2026
Break Through the Compression Bottleneck: From Theory to Practice
Xiusheng Huang, Lu Wang, Yequan Wang +2
As the parameter size of language models continues to grow, effective model compression is required to reduce their computational and memory overhead. Existing compression methods…
cs.LG2026
Theory-optimal Quantization Based on Flatness
Xiusheng Huang, Zhe Li, Xuanwu Yin +5
Post-training quantization has emerged as a widely adopted technique for compressing and accelerating the inference of Large Language Models (LLMs). The primary challenges in LLMs…