# 用于测试时选择的逐维度成对锦标赛

> 全文为英语

**Published:** 2026年11月9日
**Author:** Hannah Guan, Yilun Du
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/zh/articles/aspect-wise-pairwise-tournaments-for-test-time-selection

arXiv:2611.08640v1 通告类型：新 摘要：扩展测试时计算已被证明能高效提升LLM推理能力。一种流行方法是best-of-n采样，模型生成许多候选解并选择一个。这引出了问题：我们如何能……

arXiv:2611.08640v1 Announce Type: new Abstract: Scaling test-time compute has proven highly effective at improving LLM reasoning. A popular method is best-of-n sampling, where a model generates many candidate solutions and one is selected. This motivates the question: how can we select the best candidate? When humans make choices, we find it more natural to compare two options than judge each in isolation. Additionally, when the options become complex, we like to judge the options one aspect at a time, such as comparing two job candidates on experience, communication, etc. In the test-time setting, candidate solutions should be judged in the same way. Here we introduce Aspect-wise Pairwise Tournaments (APT), which verifies by comparison and compares by aspect. Each matchup of a single elimination tournament is decided by a vote over multiple off-the-shelf aspect verifiers, each expressing a pairwise preference along its own aspect. Across five domains and three generator LLMs, APT improves over binary multi-aspect approval, self-consistency and reward model verification, with the largest gains on open-ended tasks. We find that both components matter; neither binary approval over the same aspects nor a generic pairwise judge at a matched budget matches APT. Because aspects are specified in natural language, APT also lets users steer selection toward properties they care about, better aligning with user preferences without needing to regenerate solutions. No component is trained, so APT transfers to new domains by simply rewriting prompts.

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*内容来自原始来源： [arXiv — cs.AI](https://arxiv.org/abs/2611.08640). 查看原文.*

**标签：** #arXiv

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