Beijing, China – In a quest to optimize the reasoning capabilities of Large Language Models (LLMs), researchers at Tencent AI Lab, in collaboration with Xiamen University and Soochow University, have unveiled a new tree search framework called Fetch. This innovative approach directly addresses the critical issues of overthinking and underthinking that can plague LLMs during complex problem-solving.
The research, spearheaded by first author Wang Ante, a doctoral student at Xiamen University, and guided by corresponding authors Song Linfeng and Tu Zhaopeng from Tencent AI Lab, along with Professor Su Jinsong from Xiamen University, is detailed in a paper titled Don’t Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming Tree Search Exploration Pitfalls, available on arXiv (https://arxiv.org/abs/2502.11183).
The paper highlights the growing interest in enhancing LLM reasoning through Test-Time Computation, fueled by the impressive performance of models like OpenAI-o1. Within this burgeoning field, validator-guided tree search algorithms have emerged as a promising avenue. These algorithms systematically explore vast solution spaces, demonstrating significant advantages in finding optimal solutions for complex problems. Existing research has provided empirical support for their effectiveness, with methods like Beam Search and Best-First Search gaining traction.
However, the researchers at Tencent AI Lab and Xiamen University identified a critical gap: the potential for LLMs to either overthink or underthink during the tree search process. Overthinking can lead to unnecessary exploration of irrelevant branches, wasting computational resources and time. Conversely, underthinking can result in premature termination of the search, potentially missing the optimal solution.
The Fetch framework aims to address these pitfalls by introducing a more efficient and targeted approach to tree search. While the specifics of the framework are detailed in the research paper, the core concept revolves around intelligently guiding the search process to focus on the most promising areas of the solution space, thereby mitigating the risks of both overthinking and underthinking.
This research has significant implications for the future of LLM development and application. By optimizing the reasoning process, Fetch has the potential to unlock even greater capabilities in LLMs, enabling them to tackle more complex and nuanced problems across a wide range of domains.
The collaboration between Tencent AI Lab and leading academic institutions underscores the importance of joint efforts in pushing the boundaries of AI research. This work represents a significant step forward in the ongoing quest to build more intelligent and efficient AI systems.
References:
- Wang, A., Song, L., Tu, Z., & Su, J. (2025). Don’t Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming Tree Search Exploration Pitfalls. arXiv preprint arXiv:2502.11183. Retrieved from https://arxiv.org/abs/2502.11183
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