36 citations · 50 across the 11 of their papers we have counts for
7 papers · 1 filter
Deep Think with Confidence
Yichao Fu, Xuewei Wang, Yuandong Tian +1
Large Language Models (LLMs) have shown great potential in reasoning tasks through test-time scaling methods like self-consistency with majority voting. However, this approach ofte…
R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference
Zhenyu Zhang, Zechun Liu, Yuandong Tian +3
Large Language Models (LLMs), while demonstrating remarkable capabilities across various applications, present significant challenges during inference due to their substantial mode…
Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients
Zhenyu Zhang, Ajay Jaiswal, Lu Yin +4
Training Large Language Models (LLMs) is memory-intensive due to the large number of parameters and associated optimization states. GaLore, a recent method, reduces memory usage by…
From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications
Ajay Jaiswal, Yifan Wang, Lu Yin +6
Large Language Models' (LLMs) weight matrices can often be expressed in low-rank form with potential to relax memory and compute resource requirements. Unlike prior efforts that fo…
LoCoCo: Dropping In Convolutions for Long Context Compression
Ruisi Cai, Yuandong Tian, Zhangyang Wang +1
This paper tackles the memory hurdle of processing long context sequences in Large Language Models (LLMs), by presenting a novel approach, Dropping In Convolutions for Long Context…
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Jiawei Zhao, Zhenyu Zhang, Beidi Chen +3
Training Large Language Models (LLMs) presents significant memory challenges, predominantly due to the growing size of weights and optimizer states. Common memory-reduction approac…
JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention
Yuandong Tian, Yiping Wang, Zhenyu Zhang +2
We propose Joint MLP/Attention (JoMA) dynamics, a novel mathematical framework to understand the training procedure of multilayer Transformer architectures. This is achieved by int…