{"ID":2847311,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00640","arxiv_id":"2511.00640","title":"DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2511.00640","url_pdf":"https://arxiv.org/pdf/2511.00640v2","authors":"[\"Zicheng Xu\",\"Xiuyi Lou\",\"Guanchu Wang\",\"Yu-Neng Chuang\",\"Feng Luo\",\"Guangyao Zheng\",\"Alexander S. Szalay\",\"Zirui Liu\",\"Vladimir Braverman\"]","published":"2025-11-01T17:41:28Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CL\",\"cs.LG\"]","methods":"[\"LoRA\"]","has_code":false}