通过构建推理树结构，让大模型更高效地探索和选择优质解法。
DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching
- 用选择性分支构建推理路径骨架，避免冗余采样。
- 结合长度与准确率反相关，提前终止低效路径，平均提14%准确率。
- 小模型可超越十倍大的模型，适合资源有限的推理场景。
大型推理模型（LRMs）通过并行思考在推理阶段实现显著性能提升。然而，现有方法依赖对推理路径的冗余采样，未能有效探索推理空间以发现高质量解。为此，我们提出解码树草图（DTS），一种即插即用的解码框架，用于结构化多路径探索与推理选择。在推理探索方面，DTS通过在决策标记处选择性分枝，构建推理空间的骨干树；在推理选择方面，基于长度与准确率的负相关性，设计早期终止机制，优先保留短而可靠的路径。在四个大型推理模型和数据集上的实验表明，DTS平均提升准确率14%，减少重复生成8%。尤为关键的是，DTS使较小模型的表现超过十倍大小的模型，展现出强化推理能力的巨大潜力。
原文摘要 · Abstract (English)
Large Reasoning Models (LRMs) achieve remarkable inference-time improvements through parallel thinking. However, existing methods rely on redundant sampling of reasoning trajectories, failing to effectively explore the reasoning space to uncover high-quality solutions. To address these limitations, we propose Decoding Tree Sketching (DTS), a plug-and-play decoding framework for structural multi-trajectory exploration and reasoning selection. For reasoning exploration, DTS sketches a backbone tree of the reasoning space by selectively branching at decision tokens. For reasoning selection, guided by length-accuracy anti-correlation, DTS designs an early termination to prioritize short and reliable trajectories during decoding. Experimental results across four LRMs and datasets demonstrate that DTS significantly enhances accuracy by 14% and reduces repetitive generation by 8% on average. Notably, DTS enables smaller models to outperform larger models with 10$\times$ the size, highlighting its potential to strengthen reasoning capabilities.
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