---
id: 20260404-T0-08
title: "蒙特卡洛树搜索优化算法降低大模型推理延迟"
title_en: "Adaptive MCTS Cuts LLM Inference Latency by 40%"
url: https://ai.daily.yangsir.net/daily/20260404-T0-08
issue_date: 2026-04-04
publish_date: 2026-04-03T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2604.00510
---

# 蒙特卡洛树搜索优化算法降低大模型推理延迟

蒙特卡洛树搜索（MCTS）可提升大模型推理性能，但执行时间不稳定导致长尾延迟问题。研究提出自适应并行MCTS算法，通过动态调整搜索并行度，在保持推理准确率的同时降低40%平均延迟。该算法特别适用于实时推理场景，如在线客服、代码生成等需要快速响应的应用。开发者可直接集成此算法优化现有MCTS实现，无需额外计算资源。

## English Version

**Adaptive MCTS Cuts LLM Inference Latency by 40%**

Monte Carlo Tree Search improves LLM reasoning but suffers from variable latency. The adaptive parallel MCTS algorithm dynamically adjusts search parallelism, reducing average latency by 40% while maintaining accuracy. Ideal for real-time applications like chatbots and code generation, it can be integrated into existing MCTS implementations without extra computational overhead.

---

**来源**：[arXiv cs.AI](https://arxiv.org/abs/2604.00510)

**详情页**：https://ai.daily.yangsir.net/daily/20260404-T0-08

---

*智语观潮 · Daily — https://ai.daily.yangsir.net/llms.txt*