Think Earlier, Not Longer: Prompt Optimization via Reducing Unhealthy Exploration

While large language models exhibit strong reasoning capabilities, prior work shows that their performance can be further enhanced by encouraging greater exploration. However, existing approaches overlook the presence of unhealthy exploration that increases explorationrelated token usage without contributing to effective problem-solving. In this work, we show that prompt ambiguity can artificially prolong early-stage exploration, manifested as an elevated and delayed early-stage entropy peak. Although this uncertainty may be gradually resolved as reasoning progresses, reflected in the eventual convergence of the late-stage entropy peak, it does not meaningfully improve accuracy or self-consistency and instead substantially reduces reasoning efficiency. Motivated by these observations, we propose an entropy-dynamics-aware prompt optimization framework that trains a lightweight optimizer to generate concise clarifications. These clarifications aim to reduce ambiguity-induced earlystage uncertainty while preserving the model's reasoning capabilities. Extensive experiments across multiple models, reasoning budgets, and benchmarks demonstrate that our approach consistently improves reasoning efficiency by up to 52%, by reducing unhealthy exploration without sacrificing accuracy.