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arXiv · 2511.03808

Optimizing Reasoning Efficiency through Prompt Difficulty Prediction

Bo Zhao·Berkcan Kapusuzoglu·Kartik Balasubramaniam·Sambit Sahu·Supriyo Chakraborty·Genta Indra Winata
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Abstract

Reasoning language models perform well on complex tasks but are costly to deploy due to their size and long reasoning traces. We propose a routing approach that assigns each problem to the smallest model likely to solve it, reducing compute without sacrificing accuracy. Using intermediate representations from s1.1-32B, we train lightweight predictors of problem difficulty or model correctness to guide routing across a pool of reasoning models. On diverse math benchmarks, routing improves efficiency over random assignment and matches s1.1-32B's performance while using significantly less compute. Our results demonstrate that difficulty-aware routing is effective for cost-efficient deployment of reasoning models.

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Bo Zhao, Berkcan Kapusuzoglu, Kartik Balasubramaniam, Sambit Sahu, Supriyo Chakraborty, Genta Indra Winata. 2025-11-05. Optimizing Reasoning Efficiency through Prompt Difficulty Prediction. https://arxiv.org/abs/2511.03808

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