---
id: 20260317-T0-11
title: "论文提出平衡思维方法优化大型推理模型效率"
title_en: "Balanced Thinking Method Boosts Large Reasoning Model Efficiency"
url: https://ai.daily.yangsir.net/daily/20260317-T0-11
issue_date: 2026-03-17
publish_date: 2026-03-16T04:00:00.000Z
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2603.12372
---

# 论文提出平衡思维方法优化大型推理模型效率

arXiv论文提出平衡思维方法，解决大型推理模型在简单问题上过度思考或在复杂问题上思考不足的问题。该方法通过动态调整推理路径，减少冗余计算步骤，在基准测试中推理效率提升30%，同时保持准确性。该研究为构建更高效、更可靠的大规模推理模型提供了新思路。

## English Version

**Balanced Thinking Method Boosts Large Reasoning Model Efficiency**

arXiv paper proposes Balanced Thinking method to address overthinking and underthinking in large reasoning models. By dynamically adjusting reasoning paths, it reduces redundant computations by 30% while maintaining accuracy, offering a new approach for efficient LLM inference.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2603.12372)

**详情页**：https://ai.daily.yangsir.net/daily/20260317-T0-11

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