用动态分支搜索提升大模型推理计算，让代码生成更准更快。
Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search
- 根据外部反馈决定拓宽新路径或深入已有路径
- 在编码任务中超越重复采样和传统MCTS
- 适合需要多轮优化的复杂推理场景
近期研究表明，增加推理时计算可显著提升大语言模型（LLMs）的推理能力。尽管重复采样（即生成多个候选输出）是一种高效策略，但无法利用任务中常见的外部反馈信号进行优化，如编程任务。本文提出自适应分支蒙特卡洛树搜索（AB-MCTS），一种新型推理时框架，通过有原则的多轮探索与利用，扩展重复采样。在搜索树每个节点，AB-MCTS根据外部反馈信号动态决定“拓宽”新候选响应或“深入”重访已有响应。我们在前沿模型上评估该方法在复杂编码与工程任务中的表现。实验证明，AB-MCTS始终优于重复采样和标准MCTS，凸显结合大模型响应多样性与多轮解法优化对有效推理时扩展的重要性。代码已公开于 https://github.com/SakanaAI/treequest。
原文摘要 · Abstract (English)
Recent advances demonstrate that increasing inference-time computation can significantly boost the reasoning capabilities of large language models (LLMs). Although repeated sampling (i.e., generating multiple candidate outputs) is a highly effective strategy, it does not leverage external feedback signals for refinement, which are often available in tasks like coding. In this work, we propose Adaptive Branching Monte Carlo Tree Search (AB-MCTS), a novel inference-time framework that generalizes repeated sampling with principled multi-turn exploration and exploitation. At each node in the search tree, AB-MCTS dynamically decides whether to "go wider" by expanding new candidate responses or "go deeper" by revisiting existing ones based on external feedback signals. We evaluate our method on complex coding and engineering tasks using frontier models. Empirical results show that AB-MCTS consistently outperforms both repeated sampling and standard MCTS, underscoring the importance of combining the response diversity of LLMs with multi-turn solution refinement for effective inference-time scaling. Code is available at https://github.com/SakanaAI/treequest .
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