---
id: 20260715-T0-04
title: "免训练提升LLM推理：深度熵引导采样方法"
title_en: "Depth-Entropy Sampling Boosts LLM Reasoning Without Training"
url: https://ai.daily.yangsir.net/daily/20260715-T0-04
issue_date: 2026-07-15
publish_date: 2026-07-14T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2607.09693
---

# 免训练提升LLM推理：深度熵引导采样方法

新研究提出一种无需训练的LLM推理增强方法——深度熵引导采样。该方法不需要昂贵的强化学习训练、精心策划的数据集或奖励信号，通过调整基础模型的输出分布进行有效采样，即可提升推理能力。为降低大模型推理成本提供了新思路。

## English Version

**Depth-Entropy Sampling Boosts LLM Reasoning Without Training**

A new paper proposes Depth-Entropy Guided Sampling, a training-free method to enhance LLM reasoning. It avoids expensive RL training, curated data, and reward signals by sharpening base-model output distributions. Offers a cost-effective approach to improve reasoning.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2607.09693)

**详情页**：https://ai.daily.yangsir.net/daily/20260715-T0-04

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