Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning

Abstract

Large multimodal models have achieved strong reasoning oncomplex visual tasks, but their inference efficiency is often restricted bylong chains of thought. A promising solution is to pair a small draftmodel with a large target model, enabling cooperative inference employ-ing a routing signal that adaptively routes queries to either the draft ortarget model based on their difficulties for optimal efficiency and accu-racy. Yet, the remaining bottleneck is to establish a reliable query dif-ficulty signal under multimodal settings. Existing approaches designedfor language models either rely on post-hoc token probabilities, whichfall short in multimodal scenarios, or depend on supervised fine-tuning,which is a data-sensitive strategy. Both paradigms perform routing onlyafter a complete output, and ignore whether the target model can ac-tually solve the routed instances. To address this, we propose PRP,a Proactive Routing Paradigm that enables early decision-making byjointly evaluating the competence of both the draft and target models.Our Draft Rating Learning (DRL) equips the draft model with an inter-nal confidence estimator, while Joint Rating Learning (JRL) predictshow well the target model can handle a given query, thereby prioritiz-ing the allocation of samples it excels at rather than the hardest ones.These ratings enable fine-grained, instance-level Proactive Routingand substantially accelerate inference without compromising overall per-formance. Extensive experiments across multiple multimodal reasoningbenchmarks validate our effectiveness and efficiency.