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Muon outperforms every optimizer we tested (AdamW, SOAP, MAGMA). Multi-epoch training matters. And following work by Kotha et al. , scaling to large parameter counts works if you pair it with aggressive regularization -- weight decay up to 16x standard, plus dropout. The baseline sits at ~2.4x data efficiency against modded-nanogpt.，详情可参考体育直播
First, we iterate through the incoming state parameter rather than the local #data. That’s because if the incoming state is missing a key that #data has, we know that we don’t need to touch that key.8。业内人士推荐91视频作为进阶阅读