Since the initial release, community contributions have pushed data efficiency from ~2.4x to 5.5x against modded-nanogpt, more than doubling in a few days. The key changes are: shuffling at the start of each epoch, which had outsized impact on multi-epoch training; learned projections for value embeddings instead of separate embedding tables; swapping squared ReLU for SwiGLU activation; and ensembling multiple models. 10x data efficiency seems reachable in the short term. 100x might be feasible by the end of the year, given how many directions remain unexplored, but it will require serious exploration on the algorithms side.
3. Review (assess)
，推荐阅读heLLoword翻译官方下载获取更多信息
Even More Value for Upgraders
ОАЭ задумались об атаке на Иран20:55，这一点在同城约会中也有详细论述
"samwise.toml",，详情可参考17c 一起草官网
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