AI 自己给自己出题，反而学得更快
我们总以为 AI 该按固定标准练；这篇让 AI 一边解题、一边自己改题，而且改题时不许看自己考了多少分。研究者把「解题」和「出题」拆成两个技能，解题靠逐条反馈提升，出题则只看「有没有漏掉要求、能不能区分好坏答案」，这样出题者专挑解题者的短板，而不是把题改简单来刷分。在 5 个基准上，这套方法比现有最优平均高 2.8%–5.0%。它不是你明天能用上的，但它指向一个反直觉的方向：让 AI 自己当考官，可能比人类定死标准更有效。
📄 原文摘要(英文)
Text-space optimization adapts large language models (LLMs) by editing external natural-language artifacts rather than model weights, so the optimized artifacts remain inspectable and the model can be treated as a black box. However, most existing text-space methods keep evaluation fixed. On open-ended tasks, this can become a bottleneck: once the solver improves on the criteria a rubric measures, omitted dimensions remain invisible to the optimization signal. Simply evolving the rubric is also unreliable when updates are selected by the current solver's score, because apparent progress can come from making the rubric easier to satisfy. We introduce DecoEvo (Decoupled Co-Evolution), which co-evolves a solver skill and a rubric-generator skill under decoupled objectives without using gold rubrics during optimization. The solver skill is updated using criterion-level feedback, while the rubric-generator skill is revised through complementary audits of requirement coverage and response discrimination that are independent of aggregate solver score. This separation focuses generator updates on newly exposed solver weaknesses, reducing repeated emphasis on criteria the solver already satisfies. Under each benchmark's official evaluation, DecoEvo outperforms all compared methods across five benchmarks and three LLM backbones, yielding 2.8--5.0\% relative gains over SkillOpt in the five-benchmark average.