Chopping Trees: Semantic Similarity Based Dynamic Pruning for Tree-of-Thought Reasoning

arXiv — cs.CL•Thursday, November 13, 2025 at 5:00:00 AM

The introduction of Semantic Similarity-Based Dynamic Pruning (SSDP) marks a significant advancement in Tree-of-Thought reasoning for Large Language Models (LLMs). This innovative method addresses the computational inefficiencies caused by semantic redundancy in tree searches, allowing for real-time clustering and pruning of similar reasoning paths. SSDP achieves an impressive speedup of up to 2.3 times compared to existing tree-search methods, while still maintaining competitive accuracy, typically within 5% of the strongest baseline. Additionally, it reduces the number of explored nodes by 85-90%, showcasing its potential for enhancing the efficiency and scalability of LLMs in various reasoning benchmarks, including GSM8K and MATH500. The implementation of SSDP is publicly available, providing researchers and developers with the tools to leverage this method in their own applications.

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