Optimizing Reasoning Efficiency through Prompt Difficulty Prediction

• URL: http://arxiv.org/abs/2511.03808v1
• Date: Wed, 05 Nov 2025 19:14:53 GMT
• Title: Optimizing Reasoning Efficiency through Prompt Difficulty Prediction
• Authors: Bo Zhao, Berkcan Kapusuzoglu, Kartik Balasubramaniam, Sambit Sahu, Supriyo Chakraborty, Genta Indra Winata,
• Abstract summary: Reasoning language models perform well on complex tasks but are costly to deploy due to their size and long reasoning traces.<n>We propose a routing approach that assigns each problem to the smallest model likely to solve it, reducing compute without sacrificing accuracy.
• Score: 14.470330195517903
• License: http://creativecommons.org/licenses/by/4.0/
• 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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