---
id: 20260620-T0-09
title: "Cost-Optimal LLM Routing：在有限反馈下实现成本最优的路由策略"
title_en: "Cost-Optimal LLM Routing Strategies Under Limited User Feedback"
url: https://ai.daily.yangsir.net/daily/20260620-T0-09
issue_date: 2026-06-20
publish_date: 2026-06-19T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2606.19376
---

# Cost-Optimal LLM Routing：在有限反馈下实现成本最优的路由策略

针对LLM推理成本高昂的问题，新论文提出了Cost-Optimal Routing方案。该方法在保证用户满意度的前提下，利用有限的用户反馈来训练路由模型，动态选择最便宜且能满足SLA要求的模型。研究证明，该策略能显著降低商业应用中的Token开销，平衡了服务质量与运营成本。

## English Version

**Cost-Optimal LLM Routing Strategies Under Limited User Feedback**

Addressing high LLM inference costs, a new paper proposes Cost-Optimal Routing. The method trains a routing model using limited user feedback to dynamically select the cheapest model that meets SLA requirements while ensuring user satisfaction. Proven to reduce Token expenditure significantly in commercial settings, this strategy balances service quality with operational costs.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2606.19376)

**详情页**：https://ai.daily.yangsir.net/daily/20260620-T0-09

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