FlexRouter：为灵活 LLM 路由学习互补模型集合
FlexRouter: Learning Complementary Model Sets for Flexible LLM Routing
FlexRouter 是一个显式建模模型互补性的 LLM 路由框架，通过优化答案覆盖率来最大化所选模型中至少一个给出正确答案的概率。该方法将路由建模为面向覆盖的子集选择问题，用 Determinantal Point Processes（DPPs）刻画模型能力与冗余，并引入基于失败集边缘化的训练目标，推理时用贪心策略自适应确定子集大小。
Published on Sep 29
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Abstract
Existing Large Language Model (LLM) routing methods score LLMs independently to select top-k models. However, this ignores model correlations and enforces a rigid computational budget. Consequently, routers often select redundant models that share failure modes, limiting the overall probability of success. To address this, we propose FlexRouter, a routing framework that explicitly models model complementarity. FlexRouter optimizes for answer coverage, maximizing the probability that at least one selected model yields a correct response. This objective aligns with practical inference pipelines where multiple candidate outputs are generated and a downstream verifier or user selects the final one. We formulate routing as a coverage-oriented subset selection problem and model the routing policy using Determinantal Point Processes (DPPs), which naturally capture both model competence and redundancy. To directly optimize coverage without requiring a ground-truth target subset, we introduce a training objective based on marginalizing over failure sets. During inference, we employ a greedy strategy based on marginal log-determinant gains, enabling the router to adaptively determine subset sizes without a predefined budget. Extensive experiments on the large-scale RouterEval benchmark demonstrate that our proposed FlexRouter achieves higher coverage with lower redundancy across both in-domain and out-of-domain tasks than strong baselines while maintaining flexible inference cost.
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