NYU与Amazon论文提出SGUID技能筛选方法
先了解这件事
2026年10月10日，X用户Rohan Paul分享了纽约大学与亚马逊合作发布的一篇新论文。该论文提出名为SGUID的方法，用于筛选技能库中的技能。论文发现，只蒸馏那些能持续产生有用训练信号的少量技能，其效果可以匹配甚至超过蒸馏一个规模大至11倍的技能库。报道中举例说明，技能是简短的书面提示，例如一条计数规则。这是目前关于该论文的最新披露信息，尚无后续进展报道。
报道时间线
- NYU 与 Amazon 论文提出 SGUID 筛选技能库方法
New NYU and Amazon paper finds that distilling a few skills that keep producing a useful training signal matches or beats distilling a skill bank up to 11× larger. Skills are short written tips, like a rule for counting cases, that a model absorbs by learning from a copy of itself that reads them. Picked by topic match, under 25% of them gave any useful signal across 3 Qwen models. SGUID keeps only skills that help early in training and still help late. With 6 such skills, 3 of 4 models matched or beat the full bank of 30 to 71 skills on math contest tests. A 2nd round with 3 new skills lifted Qwen3-8B from 64.3% to 66.3%.