Routing Starts with Representation: Behavioral Query Pretraining for LLM Routers

Large language models (LLMs) vary substantially in capability, making model routing a promising approach to improving inference efficiency by selecting an appropriate model for each query. Existing work has largely focused on designing more capable routers, while treating the query representation as a fixed input from a general-purpose text encoder. We show that this assumption is limiting: semantic query geometry is systematically misaligned with the behavioral geometry relevant to routing. Motivated by this observation, we introduce behavioral query pretraining, which uses historical query–model interactions to fine-tune a query encoder with multi-model supervision and then discards the prediction head, yielding a reusable routing-aware representation. Across diverse frozen-query routing methods, the resulting representation consistently improves routing performance and is effective under limited target supervision.