English(EN) Policy-Guided Stepwise Model Routing for Cost-Effective Reasoning

AI模型使用策略引导路由进行成本效益数学推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-08 04:00

研究人员通过实施策略引导分步模型路由系统，开发了一种在大型语言模型中实现成本效益推理的新方法。该方法将中间思维链状态路由到不同大小模型的任务，构建为一个受约束的决策问题。通过使用强化学习训练一个小型控制策略并采用阈值校准，该系统优化了性能-效率的权衡，优于手工制作的策略，并能与训练了更大奖励模型的方法相媲美。 AI

影响该方法可能导致更高效、更具成本效益的大型语言模型在复杂推理任务中的部署。

排序理由这是一篇详细介绍一种新颖的改进大型语言模型推理效率方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Wenwen Si, Insup Lee, Osbert Bastani · 2026-05-08 04:00

面向成本效益推理的策略引导分步模型路由

arXiv:2605.06116v1 Announce Type: new Abstract: Inference-time computation has greatly enhanced the performance of large language models (LLMs) on challenging reasoning tasks, but this strategy can incur high inference costs. One solution is to route intermediate chain-of-thought…

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面向成本效益推理的策略引导分步模型路由

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