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Talent Scout

Z

Zhengbo Wang

University of Science and Technology of China

J

Jian Liang

Institute of Automation, Chinese Academy of Sciences

R

Ran He

Institute of Automation, Chinese Academy of Sciences

Z

Zilei Wang

University of Science and Technology of China

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References (40)

[1]
AdaPM: a Partial Momentum Algorithm for LLM Training
2025Yimu Zhang, Yuanshi Liu et al.
[2]
Low-rank Momentum Factorization for Memory Efficient Training
2025Pouria Mahdavinia, Mehrdad Mahdavi
[3]
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
2025Gheorghe Comanici, Eric Bieber et al.
[4]
LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning
2025Nurbek Tastan, Stefanos Laskaridis et al.
[5]
Qwen3 Technical Report
2025An Yang, Anfeng Li et al.
[6]
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
2025Adam Suma, Samuel Dauncey
[7]
MLorc: Momentum Low-rank Compression for Large Language Model Adaptation
2025Wei Shen, Yaxiang Zhang et al.
[8]
Parameter and Memory Efficient Pretraining via Low-rank Riemannian Optimization
2025Zhanfeng Mo, Long-Kai Huang et al.
[9]
SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training
2024Chao Ma, Wenbo Gong et al.
[10]
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training
2024Philip Zmushko, A. Beznosikov et al.
[11]
LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization
2024Jui-Nan Yen, Si Si et al.
[12]
Fira: Can We Achieve Full-rank Training of LLMs Under Low-rank Constraint?
2024Xi Chen, Kaituo Feng et al.
[13]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[14]
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
2024Zhengbo Wang, Jian Liang
[15]
LoRA-GA: Low-Rank Adaptation with Gradient Approximation
2024Shaowen Wang, Linxi Yu et al.
[16]
SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining
2024Andi Han, Jiaxiang Li et al.
[17]
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
2024Jiawei Zhao, Zhenyu (Allen) Zhang et al.
[18]
LoRA+: Efficient Low Rank Adaptation of Large Models
2024Soufiane Hayou, Nikhil Ghosh et al.
[19]
DoRA: Weight-Decomposed Low-Rank Adaptation
2024Shih-Yang Liu, Chien-Yi Wang et al.
[20]
A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
2024Zhengbo Wang, Jian Liang et al.

Showing 20 of 40 references

Founder's Pitch

"LoRA-Pre is a memory-efficient optimizer leveraging low-rank approximation to reduce memory usage while maintaining or exceeding performance in training large language models."

Optimization TechnologyScore: 8View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

3/4 signals

7.5

Series A Potential

4/4 signals

10

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Why It Matters

This research matters because it addresses the significant memory overhead in training large language models, making the process more efficient and scalable.

Product Angle

Offer LoRA-Pre as a subscription-based tool or API that AI developers and organizations can integrate to optimize training processes, reducing costs and enhancing performance.

Disruption

LoRA-Pre could replace current memory-intensive optimizers like Adam by providing a more efficient alternative that requires significantly less memory without sacrificing performance.

Product Opportunity

The rising cost and resource demand of training large models is a critical pain point. The optimizers market for AI is expanding, and reducing memory usage offers direct cost-saving benefits to any company training large models.

Use Case Idea

Commercialize LoRA-Pre as an optimizer plugin for AI development platforms focusing on efficiency and cost reduction in large model training environments.

Science

The paper introduces LoRA-Pre, which uses low-rank approximation to compress the momentum states in optimizers like Adam. This reduces memory usage while maintaining the performance of LLMs during pre-training and fine-tuning by treating momentum as an online linear regression problem.

Method & Eval

LoRA-Pre was validated by pre-training models of different sizes within the Llama architecture. It demonstrated superior performance to baselines while using a much lower rank, significantly reducing memory overhead.

Caveats

A potential limitation is the assumption of linear regression equivalence for all scenarios and the dependency on low-rank conditions which might not hold for every type of data or model architecture.

Author Intelligence

Zhengbo Wang

University of Science and Technology of China
zhengbowang@mail.ustc.edu.cn

Jian Liang

Institute of Automation, Chinese Academy of Sciences
liangjian92@gmail.com

Ran He

Institute of Automation, Chinese Academy of Sciences

Zilei Wang

University of Science and Technology of China

Tieniu Tan

Nanjing University