2 citations · 2 across the 2 of their papers we have counts for
4 papers · 1 filter
Towards a Comprehensive Scaling Law of Mixture-of-Experts
Guoliang Zhao, Yuhan Fu, Shuaipeng Li +10
Mixture-of-Experts (MoE) models have become the consensus approach for enabling parameter-efficient scaling and cost-effective deployment in large language models. However, existin…
Proximal Supervised Fine-Tuning
Wenhong Zhu, Ruobing Xie, Rui Wang +3
Supervised fine-tuning (SFT) of foundation models often leads to poor generalization, where prior capabilities deteriorate after tuning on new tasks or domains. Inspired by trust-r…
TransMamba: A Sequence-Level Hybrid Transformer-Mamba Language Model
Yixing Li, Ruobing Xie, Zhen Yang +8
Transformers are the cornerstone of modern large language models, but their quadratic computational complexity limits efficiency in long-sequence processing. Recent advancements in…
Scaling Laws for Floating Point Quantization Training
Xingwu Sun, Shuaipeng Li, Ruobing Xie +13
Low-precision training is considered an effective strategy for reducing both training and downstream inference costs. Previous scaling laws for precision mainly focus on integer qu…