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In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.

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numbers are sometimes later products but not always; some prefixes mean specific

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另外,瞄准2030年前实现中国人首次登陆月球的目标,载人月球探测工程登月阶段任务各项研制建设工作正在扎实稳步推进。

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