EntroRouter: Learning Efficient Model Routing via Entropy Regulation
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Computer Science > Computation and Language
arXiv:2606.29424 (cs)
[Submitted on 28 Jun 2026]
Title:EntroRouter: Learning Efficient Model Routing via Entropy Regulation
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Abstract:Model routing balances solution accuracy and computational cost by selecting among models of varying capabilities. While recent multi-round frameworks interleave reasoning and planning, we identify a structural failure mode termed Trust Region Collapse. We demonstrate that the deep coupling of reasoning and routing, exacerbated by the dominance of strong pre-training priors under sparse supervision, leads to degenerate local optima where capable experts are systematically suppressed. To decouple these processes, we propose $\textbf{EntroRouter}$, a single-round routing framework that treats entropy regulation as a core objective. We first initialize the policy via Soft Supervision, fitting a distribution of suitable models to establish a high-entropy prior for exploration. Subsequently, we stabilize Reinforcement Learning using a Soft Anchor, which utilizes offline capability estimates to orchestrate controlled entropy contraction within a safe trust region. Extensive experiments demonstrate that EntroRouter retains 98.3% of the strongest expert's accuracy while reducing computational costs by 48.25%.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.29424 [cs.CL] |
| (or arXiv:2606.29424v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.29424
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled EntroRouter: Learning Efficient Model Routing via Entropy Regulation, by Kaiyi Zhang and 3 other authors
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