English(EN) Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems

新理论解释了LLM采样策略中的缩放

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

一篇新的arXiv论文引入了“尾部竞争”作为一种框架，用于理解大型语言模型中具有不完整奖励模型的最佳N采样的工作原理。该研究使用极值理论来识别三种不同的状态：正确尾部占优、错误尾部占优和临界状态。它推导出了缩放定律，表明即使平均奖励较低，错误响应有时也会主导选择，并提出了一个“尾部张力”标准来预测何时额外的采样可能是有害的。 AI

影响为LLM采样策略提供了理论理解，可能导致更有效的推理技术。

排序理由学术论文发表在arXiv上，详细介绍了LLM采样的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Taro Yano, Yoichi Ishibashi, Masafumi Oyamada · 2026-10-15 04:00

尾部竞争解释可验证问题中最佳N采样中的缩放

arXiv:2610.19362v1 Announce Type: new Abstract: Best-of-N sampling is a simple inference-time scaling strategy for large language models, yet its behavior under incomplete reward models remains poorly understood. We study Best-of-N as a competition between the extreme reward tail…

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报道来源 [1]

尾部竞争解释可验证问题中最佳N采样中的缩放

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