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一只极其丰满的“老虎”骄傲地穿过舞池，Maggie姐不屑地撇过头，“隆的。”像Maggie这样的上一代香港妈咪，对内地小姐的态度确有几分微妙：她们喊价低，又拼命，很快就把传统的夜总会小姐比下去，再看看她们的外形，哪一个不是浓妆艳抹、凹凸有致。连Maggie姐也惊叹于这些内地女孩的拼命和坚韧：香港夜总会黄金时期，一大批内地女孩来香港淘金，中国城、大富豪都有内地小姐的身影，狠命做几年就挣到了第一桶金，然后金盆洗手，在香港嫁人，或回老家做生意。“她们很有规划，知道自己要什么，所以叫老虎，很厉害啊！”
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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.