RouteWeaver: 统一 LLM 路由模式选择与执行
RouteWeaver: Weaving Mode Selection and Execution into Unified LLM Routing
RouteWeaver 通过协同训练和渐进式探索统一了异构 LLM 路由模式，在多种推理任务中提升了准确性；其平均准确率达到 61.4%，相比最强基线相对提升 11.03%，并在五个领域均超越所有对比基线。
Published on Oct 19
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Abstract
RouteWeaver unifies heterogeneous LLM routing modes via coordinated training and progressive exploration, improving accuracy across diverse reasoning tasks.
LLM routing dynamically selects and coordinates heterogeneous language models across diverse tasks. However, existing approaches are largely built around a predefined execution paradigm, such as single-round, multi-round, or agentic routing, which fixes how model calls are organized and limits adaptation to different query needs. We propose RouteWeaver, a unified routing framework that integrates these routing modes within a single LLM policy, jointly learning which mode to use and how to select models within that mode. To jointly optimize mode selection and within-mode execution, we develop COMET-GRPO (COordinated Mode-Execution Training), which decouples the trajectory-level advantage into separate signals for mode selection and within-mode execution, enabling more targeted credit assignment to each decision level. We further introduce progressive exploration, which gradually shifts training from externally assigned modes to autonomous selection, so that the router first learns how to execute each mode before deciding when to use it. We evaluate RouteWeaver across mathematics, coding, knowledge, logical reasoning, and factual recall. RouteWeaver achieves an average accuracy of 61.4%, an 11.03% relative improvement over the strongest baseline, and outperforms all compared baselines in each of the five domains. Ablations show that both COMET-GRPO and progressive exploration are important for adaptive routing; without progressive exploration, the routing policy collapses entirely to single-round execution.
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