---
id: 20260522-T0-13
title: "LBW-Guard训练控制系统：高压力下保持模型稳定性"
title_en: "LBW-Guard: Training Control Under Stress for Model Stability"
url: https://ai.daily.yangsir.net/daily/20260522-T0-13
issue_date: 2026-05-22
publish_date: 2026-05-21T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2605.19008
---

# LBW-Guard训练控制系统：高压力下保持模型稳定性

arXiv论文提出Learn-by-Wire Guard训练控制系统，解决大规模模型训练不稳定问题。在高学习率、大规模计算和运行压力下，训练常出现崩溃和计算浪费。该系统通过动态调整学习策略，在保持训练效率的同时将模型崩溃率降低40%。特别适用于千亿参数模型的高效训练场景，已通过GPT-3规模模型验证。

## English Version

**LBW-Guard: Training Control Under Stress for Model Stability**

arXiv paper presents Learn-by-Wire Guard (LBW-Guard) to address LLM training instability. Under aggressive learning rates and scale, training often crashes and wastes compute. This system dynamically adjusts learning policies, reducing crash rates by 40% while maintaining efficiency. Validated on GPT-3 scale models, it's particularly suitable for training billion-parameter models.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2605.19008)

**详情页**：https://ai.daily.yangsir.net/daily/20260522-T0-13

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