aiqbench analysis
This study identifies a fundamental "routing plateau" where current LLM routing methods converge on suboptimal accuracy despite extensive benchmarking and training improvements. The authors attribute this bottleneck to routers learning global model trends rather than capturing fine-grained, query-specific signals necessary for instance-level decisions. Future progress likely requires incorporating additional inputs, such as partial output trajectories, beyond the initial query text to break through these performance limits.
The Routing Plateau: Understanding the Accuracy Limits of LLM Routers
arXiv:2606.07587v2 Announce Type: replace Abstract: LLM routing has become a popular approach to improve the cost-quality trade-off of LLM services by adaptively selecting a model for each query. Recent work has explored a broad range of routing methods, including clustering-based routers, learned classifiers, pairwise ranking, and confidence-based approaches. Our extensive study of 21 routing methods across five benchmarks reveals a consistent phenomenon that we call the routing plateau (Fig. 1): many methods, including kNN, achieve very similar accuracy and converge to a narrow performance range that remains far below the oracle router. Our analysis supports a correctness-prediction bottleneck hypothesis: current routers primarily learn global-average model performance trends rather than fine-grained, query-specific routing signals. As a result, they collectively fail on queries that require instance-specific routing decisions. Moreover, to understand whether the plateau can be alleviated with a better training setup, we construct a 300K-query benchmark (Nine-by-300k). More data, larger encoders, and end-to-end fine-tuning improve eight routers by 1.24 pp on average, but leave the plateau largely intact. These findings suggest that further progress may require inputs beyond the query itself, such as partial output trajectories that reveal how models attempt the task.
Abstract:LLM routing has become a popular approach to improve the cost-quality trade-off of LLM services by adaptively selecting a model for each query. Recent work has explored a broad range of routing methods, including clustering-based routers, learned classifiers, pairwise ranking, and confidence-based approaches. Our extensive study of 21 routing methods across five benchmarks reveals a consistent phenomenon that we call the routing plateau (Fig. 1): many methods, including kNN, achieve very similar accuracy and converge to a narrow performance range that remains far below the oracle router. Our analysis supports a correctness-prediction bottleneck hypothesis: current routers primarily learn global-average model performance trends rather than fine-grained, query-specific routing signals. As a result, they collectively fail on queries that require instance-specific routing decisions. Moreover, to understand whether the plateau can be alleviated with a better training setup, we construct a 300K-query benchmark (Nine-by-300k). More data, larger encoders, and end-to-end fine-tuning improve eight routers by 1.24 pp on average, but leave the plateau largely intact. These findings suggest that further progress may require inputs beyond the query itself, such as partial output trajectories that reveal how models attempt the task.
Machine Learning (cs.LG)
arXiv:2606.07587 [cs.LG]
(or arXiv:2606.07587v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.07587 Focus to learn more arXiv-issued DOI via DataCite
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