AdaThinking-E: One-Token Entropy Regulation for Adaptive Thinking

Multimodal large language models have demonstrated strongdocument reasoning capabilities by incorporating explicit thinking pro-cesses. While this capability significantly improves performance on chal-lenging tasks, current models apply such deep reasoning uniformly to allquestions, resulting in unnecessary computational overhead for simpletask. This not only degrades user experience but also negatively impactaccuracy on benchmark datasets. We identify the critical need for adap-tive thinking mechanisms that can intelligently determine when to en-gage reasoning based on question complexity. To address this, we proposeAdaThinking-E, a novel reinforcement learning framework that learnsadaptive thinking through one-token entropy regulation. Our key insightis that model confidence in the decision to engage thinking (or not)can be quantified through entropy analysis of the predicted probabilitydistribution at critical decision tokens. This observation motivates ourentropy-governed reward mechanism: the training process naturally tran-sitions from high-entropy exploration, where the model experiments withdifferent thinking strategies, to low-entropy convergence with confident,generalizable decision-making policies. Crucially, this approach enablesmodels to intrinsically discover when to think without requiring manualintervention or external difficulty labels. Extensive experiments demon-strate that our approach enables models to be both accurate on complexproblems and efficient on simple ones across diverse document tasks.Code is available at https://github.com/PriNing/AdaThinking-E.