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References (31)

[1]
Orthogonal Finetuning Made Scalable
2025Zeju Qiu, Weiyang Liu et al.
[2]
Reparameterized LLM Training via Orthogonal Equivalence Transformation
2025Zeju Qiu, Simon Buchholz et al.
[3]
GaLore 2: Large-Scale LLM Pre-Training by Gradient Low-Rank Projection
2025DiJia Su, Andrew Gu et al.
[4]
Muon is Scalable for LLM Training
2025Jingyuan Liu, Jianling Su et al.
[5]
CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation
2025Z. Liu, Ruijie Zhang et al.
[6]
Gradient Weight-normalized Low-rank Projection for Efficient LLM Training
2024Jia-Hong Huang, Yixian Shen et al.
[7]
GaLore+: Boosting Low-Rank Adaptation for LLMs with Cross-Head Projection
2024Xu Liao, Shaohui Li et al.
[8]
APOLLO: SGD-like Memory, AdamW-level Performance
2024Hanqing Zhu, Zhenyu (Allen) Zhang et al.
[9]
Fira: Can We Achieve Full-rank Training of LLMs Under Low-rank Constraint?
2024Xi Chen, Kaituo Feng et al.
[10]
Memory-Efficient LLM Training with Online Subspace Descent
2024Kaizhao Liang, Bo Liu et al.
[11]
Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients
2024Zhenyu (Allen) Zhang, A. Jaiswal et al.
[12]
SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining
2024Andi Han, Jiaxiang Li et al.
[13]
VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections
2024Roy Miles, Pradyumna Reddy et al.
[14]
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
2024Jiawei Zhao, Zhenyu (Allen) Zhang et al.
[15]
Training Neural Networks from Scratch with Parallel Low-Rank Adapters
2024Minyoung Huh, Brian Cheung et al.
[16]
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
2023Weiyang Liu, Zeju Qiu et al.
[17]
ReLoRA: High-Rank Training Through Low-Rank Updates
2023Vladislav Lialin, Namrata Shivagunde et al.
[18]
Controlling Text-to-Image Diffusion by Orthogonal Finetuning
2023Zeju Qiu, Wei-yu Liu et al.
[19]
SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models
2023Vithursan Thangarasa, Abhay Gupta et al.
[20]
Monarch: Expressive Structured Matrices for Efficient and Accurate Training
2022Tri Dao, Beidi Chen et al.

Showing 20 of 31 references

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