当路由崩溃：关于LLM路由的退化收敛
When LLM Routers Overpay: The Cost of Strong-Model Preference
AI总结：
本文提出EquiRouter，一种决策意识的路由器，通过直接学习模型排名来缓解LLM路由中的路由崩溃问题，有效降低计算和货币成本。
摘要：
LLM路由旨在通过动态地将简单的查询分配给较小的模型和困难的查询分配给更强的模型来实现有利的质量-成本权衡。然而，在单模态和多模态设置中，我们发现现有路由器中存在一个普遍存在但未被充分探索的失败模式：随着用户成本预算的增加，路由器系统性地倾向于最强大且最昂贵的模型，即使更便宜的模型已经足够。因此，当前的路由器未能充分利用小型模型，浪费了计算和货币成本，并削弱了路由的核心承诺；我们称这种现象为路由崩溃。我们将路由崩溃归因于目标-决策不匹配：许多路由器被训练来预测标量性能分数，而路由决策最终依赖于候选模型之间的离散比较。因此，小型预测误差可以翻转相对顺序并触发次优选择。为了弥补这一差距，我们提出了EquiRouter，一种决策意识的路由器，直接学习模型排名，恢复小型模型的作用并减轻路由崩溃。在RouterBench上，与最强的先前路由器相比，EquiRouter在GPT-4级性能下将成本降低了约17%。我们的代码可在https://github.com/AIGNLAI/EquiRouter上获得。
英文摘要：
LLM routing aims to reduce inference costs by assigning easy queries to less expensive models and reserving stronger models for cases in which they are needed. However, we find that existing routers often violate this principle under loose cost budgets: as the budget increases, they increasingly select the strongest and most expensive model even when less expensive feasible candidates provide comparable or identical utility. We refer to this behavior as strong-model over-selection under loose budgets. Our analysis shows that ambiguity alone does not explain this phenomenon. Substantial overpayment can arise when differently priced models offer equivalent or near-equivalent utility and the router retains a persistent model-level preference for costly models; budget relaxation exposes this preference by making premium candidates feasible. Motivated by this diagnosis, we propose EquiRouter, a decision-aligned router that learns query-dependent model rankings, preserves meaningful utility differences, and explicitly prefers lower-cost models among equivalent candidates through a Cost-Aware Ranking Objective and a lightweight Query--Model Interaction Representation. Experiments across diverse text, multimodal, continuous-utility, and large-model-pool settings show that EquiRouter consistently improves cost efficiency while maintaining competitive peak and whole-curve performance. Code is available at https://github.com/LAMDA-Model-Reuse/EquiRouter.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
