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arXiv · 2601.15296

Entropy-Tree: Tree-Based Decoding with Entropy-Guided Exploration

Longxuan Wei·Yubo Zhang·Zijiao Zhang·Zhihu Wang·Shiwan Zhao·Tianyu Huang·Huiting Zhao·Chenfei Liu·Shenao Zhang·Junchi Yan
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Abstract

Large language models achieve strong reasoning performance, yet existing decoding strategies either explore blindly (random sampling) or redundantly (independent multi-sampling). We propose Entropy-Tree, a tree-based decoding method that exploits entropy as a signal for branching decisions--expanding the search tree only at positions where the model exhibits genuine uncertainty. Entropy-Tree shows superior accuracy and calibration in reasoning tasks: it achieves better pass@k than Multi-chain across multiple models and datasets, and its predictive entropy demonstrates better AUROC compared to several traditional metrics. Entropy-Tree unifies efficient structured exploration and reliable uncertainty estimation within a single decoding procedure.

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Longxuan Wei, Yubo Zhang, Zijiao Zhang, Zhihu Wang, Shiwan Zhao, Tianyu Huang, Huiting Zhao, Chenfei Liu, Shenao Zhang, Junchi Yan. 2026-01-02. Entropy-Tree: Tree-Based Decoding with Entropy-Guided Exploration. https://arxiv.org/abs/2601.15296

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