AI 优化 GPU 代码，学到的经验换个语言就废了
让 AI 优化 GPU 程序，各家都在攒经验：把一次调优中学到的东西存成知识库或技能，下次直接用。但没人测过一件事——这些经验换个编程语言还灵不灵。新基准 MetaKernelBench 专门测这个：同一道题，用 CuTe 和 TIRx 两种语言各做一遍，第一遍做完把经验写成自然语言笔记，第二遍只靠这份笔记重新解题。结果很微妙：六款模型里，四款在两个方向都有提升，但每个模型都有 16% 到 45% 的题反而退步。关键不是源任务成不成功，而是源任务的表现比目标任务单独做时好还是差——源表现更好时，71% 的情况有提升；源更差时，54% 的情况会退步。换句话说，AI 攒的经验不是万能钥匙，它更像一把只配某些锁的钥匙，用错了反而拖后腿。这不是你明天能用上的东西，但它提醒我们：AI 的“学习”和人的学习一样，换了个环境，经验可能变成包袱。
📄 原文摘要(英文)
Recent GPU kernel optimization agents retain what they learn in knowledge bases or as distilled skills. Kernel benchmarks score each attempt's implementation for correctness and speed but leave the reuse value of retained experience unmeasured. We introduce MetaKernelBench, which measures whether experience distilled from an attempt in one kernel domain-specific language (DSL) improves a fresh attempt at the same problem in another. Its 74 problems are fused subgraphs in six families, each posed as a pair of CuTe DSL and TIRx variants that differ only in the DSL. The agent first attempts each variant solo and is instructed to distill what it learns into a natural-language skill, which is transferred whether or not the source attempt passes verification. The skill is the only extra input to a skill-conditioned attempt by the same model in the other DSL. We compare each skill-conditioned attempt with the solo attempt on the same variant under matched per-attempt budgets, scoring correctness and end-to-end runtime. Across six models and both directions, paired lift over solo attempts ranges from -19% to +29%. Four models gain in both directions, yet regressions occur on 16% to 45% of problems in every model and direction. Outcomes follow the source attempt's result relative to the target's solo attempt rather than source success alone, improving in 71% of comparisons when the source stands above and regressing in 54% when it stands below. MetaKernelBench complements implementation-quality metrics by measuring same-problem cross-DSL kernel knowledge transfer.