Unverified50% confidenceSolutionExact time
QK Normalization（对Attention中的Query和Key分别做LayerNorm）被证明能显著提升超大规模模型的训练稳定性，已被Gemma 2等模型采用
1
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50%
Confidence
Long-term
Relevance
7/16/2026
First Seen
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Unverified层归一化（Layer Normalization）在训练过程中稳定各层的数值范围70% similarVerified量化感知训练（QAT）在训练过程中模拟量化误差反向传播，效果通常优于训练后量化（PTQ）70% similarUnverifiedVersatIL 支持量化感知训练（QAT）和训练后量化，借助 torchao 实现65% similarUnverifiedQAT通常需要在原始训练数据上微调10%~20%的epoch，且对学习率调度敏感65% similarUnverified微软BitNet等研究表明，若从预训练阶段采用1Bit量化感知训练（QAT），精度损失可控，而事后量化（PTQ）损失通常更大64% similar
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