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News/SAGE: Sign-Adaptive Gradient for Memory-Efficient LLM Optimization
arxiv
PublishedApril 10, 2026 at 4:00 AM
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SAGE: Sign-Adaptive Gradient for Memory-Efficient LLM Optimization

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Publisher summary· verbatim

arXiv:2604.07663v1 Announce Type: new Abstract: The AdamW optimizer, while standard for LLM pretraining, is a critical memory bottleneck, consuming optimizer states equivalent to twice the model's size. Although light-state optimizers like SinkGD attempt to address this issue, we identify the embedd

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    Llama-3.1-70B
    meta-llama/Llama-3.1-70B
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    Llama-3.1-70B
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#optimization#memory-efficiency#large-language-models
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