现有大模型路由系统在预算增加时会盲目选最强模型，导致浪费算力。
When Routing Collapses: On the Degenerate Convergence of LLM Routers
- 设计直接学习模型排序的决策感知路由机制
- 在同等性能下降低约17%计算成本
- 适合关注推理效率与成本优化的研究者
大模型路由旨在通过动态分配简单任务给小模型、复杂任务给强模型，实现质量与成本的最优权衡。然而，在单模态和多模态设置中，我们发现现有路由系统存在普遍但未被充分研究的失效模式：随着用户预算增加，路由系统会系统性地默认选择最强大且最昂贵的模型，即使更便宜的模型已足够胜任。这导致小模型被严重低估，造成计算和资金浪费，背离了路由的核心优势；我们称此现象为路由坍塌。其根源在于目标与决策不匹配：多数路由模型训练目标是预测标量性能得分，而实际路由决策依赖于候选模型间的离散比较。微小的预测误差即可改变相对顺序，引发次优选择。为此，我们提出EquiRouter，一种直接学习模型排序的决策感知路由方法，重新激活小模型的作用，缓解路由坍塌问题。在RouterBench基准上，EquiRouter在达到GPT-4级性能时，相比最强基线路由器降低约17%成本。代码已开源：https://github.com/AIGNLAI/EquiRouter。
原文摘要 · Abstract (English)
LLM routing aims to achieve a favorable quality--cost trade-off by dynamically assigning easy queries to smaller models and harder queries to stronger ones. However, across both unimodal and multimodal settings, we uncover a pervasive yet underexplored failure mode in existing routers: as the user's cost budget increases, routers systematically default to the most capable and most expensive model even when cheaper models already suffice. As a result, current routers under-utilize small models, wasting computation and monetary cost and undermining the core promise of routing; we term this phenomenon routing collapse. We attribute routing collapse to an objective--decision mismatch: many routers are trained to predict scalar performance scores, whereas routing decisions ultimately depend on discrete comparisons among candidate models. Consequently, small prediction errors can flip relative orderings and trigger suboptimal selections. To bridge this gap, we propose EquiRouter, a decision-aware router that directly learns model rankings, restoring the role of smaller models and mitigating routing collapse. On RouterBench, EquiRouter reduces cost by about 17\% at GPT-4-level performance compared to the strongest prior router. Our code is available at https://github.com/AIGNLAI/EquiRouter.
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