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arXiv Machine Learning By Zining Wang, Tongkun Guan, Boming Chen, Zhentao Guo, Jianqiang Liu, Chao Jin, Chen Duan, Kai Zhou, Pengfei Yan, Wei Shen, Xiaokang Yang

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

Read the original on arXiv Machine Learning →

AdaThinking-E introduces a reinforcement learning framework that uses one-token entropy regulation to enable large language models to decide adaptively whether to engage in deep reasoning. By measuring entropy in the predicted probability distribution at key decision tokens, the model learns to explore different thinking strategies during training and converge to confident, efficient decision policies. Experiments show the method improves accuracy on complex tasks while reducing computational overhead on simpler ones across various document reasoning benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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