4 citations · 4 across the 15 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2025
QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Wei Huang, Yi Ge, Shuai Yang +11
We propose QeRL, a Quantization-enhanced Reinforcement Learning framework for large language models (LLMs). While RL is essential for LLMs' reasoning capabilities, it is resource-i…
cs.LG2025
MC#: Mixture Compressor for Mixture-of-Experts Large Models
Wei Huang, Yue Liao, Yukang Chen +6
Mixture-of-Experts (MoE) effectively scales large language models (LLMs) and vision-language models (VLMs) by increasing capacity through sparse activation. However, preloading all…
cs.LG2025
DBellQuant: Breaking the Bell with Double-Bell Transformation for LLMs Post Training Binarization
Zijian Ye, Wei Huang, Yifei Yu +3
Large language models (LLMs) demonstrate remarkable performance but face substantial computational and memory challenges that limit their practical deployment. Quantization has eme…