大模型不用改参数，靠“外挂”就能变聪明
一个更强的AI可以给一个更弱的AI搭一套“脚手架”，让后者在考试时几乎翻倍地提升成绩——平均从0.49涨到0.91，而且完全不用动后者的参数。做法是：强模型拿5%的题目当验证集，反复调整一套“答题流程”，比如把容易出错的推理步骤写成确定性代码、按题目类型分流、强制输出格式。真正起作用的不是让弱模型多想多试，而是把不稳定的思考外包给代码。这更像“给差生配了套答题模板”，而不是“把差生教聪明”。它不是你明天就能用的功能，但它提示了一条新路：以后强模型的价值，可能不只是直接回答问题，而是给弱模型设计“外挂”。
📄 原文摘要(英文)
Recent work on distillation transfers the capabilities of large models to smaller ones often by updating the latter's parameters, through teacher forcing, on-policy distillation, and related training-time methods. In this paper, we ask whether such transfer can instead occur at test time. We study strong-to-weak scaffolding: whether a stronger builder model can construct inference-time harnesses that help a weaker target model solve tasks more reliably without any parameter updates. Using four representative Theory-of-Mind benchmarks, each builder model uses 5% of the data as a validation set to iteratively refine its harness over multiple rounds, after which the finalized harness is evaluated on the full test set. Empirically, this form of test-time capability transfer is highly effective, nearly doubling average target-model performance from 0.49 to 0.91. Our analysis shows that the gains come primarily from offloading unstable model reasoning into deterministic code, benchmark-specific routing, and strict answer-format enforcement, rather than from encouraging the target model to reason more extensively or sample more broadly. We further find that builder-model reasoning effort improves harness quality monotonically, platform effects are modest relative to the builder model's own capability, and weaker target models receive the largest gains. These results suggest that inference-time harness design is an important complement to conventional training-time distillation, enabling strong models to transfer cognitive structure to weaker models without retraining.