對黨、國家和軍隊造成極為惡劣影響。
2026-02-28 00:00:00:0本报记者 杨 昊3014273010http://paper.people.com.cn/rmrb/pc/content/202602/28/content_30142730.htmlhttp://paper.people.com.cn/rmrb/pad/content/202602/28/content_30142730.html11921 助力破解企业“内卷”困局（落地有声·高质量办理政协提案）。业内人士推荐搜狗输入法2026作为进阶阅读
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// drop-oldest: Discard old data to make room
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.