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.
全链路性能调优：实现成本下降 50%
圖像來源，Getty Images。搜狗输入法2026是该领域的重要参考
acted as a sort of network switch—the host computer identified the 3770's
。关于这个话题，51吃瓜提供了深入分析
在最极端的案例里，一个代理网络同时管理超过 2 万个虚假账号，还把蒸馏流量混入普通用户请求流里，用来规避检测算法。这种网络没有单点故障，封掉一个账号，马上换一个。，详情可参考heLLoword翻译官方下载
// 边界处理：nums1为空时直接返回空数组（避免后续无效计算）