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News/STQuant: Spatio-Temporal Adaptive Framework for Optimizer Quantization in Large Multimodal Model Training
arxiv
PublishedApril 9, 2026 at 4:00 AM
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STQuant: Spatio-Temporal Adaptive Framework for Optimizer Quantization in Large Multimodal Model Training

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.06836v1 Announce Type: new Abstract: Quantization is an effective way to reduce the memory cost of large-scale model training. However, most existing methods adopt fixed-precision policies, which ignore the fact that optimizer-state distributions vary significantly across layers and train

Models mentioned
01
  • 01AI organization logo
    gpt2
    gpt2
Related
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  • arxiv9d
    How Much Orthogonalization Does Muon Need?
  • arxivMay 1
    Making Logic a First-Class Citizen in Generative ML for Networking
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Discussion
Mentioned models
02
  • 01
    gpt2
    gpt2
  • 02
    ViT
Source
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arxiv
Read original ↗All from arxiv →
Tags
04
#optimization#quantization#memory-reduction#deep-learning

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Mentioned models
02
  • 01
    gpt2
    gpt2
  • 02
    ViT
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#optimization#quantization#memory-reduction#deep-learning

Related coverage

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