---
id: 20260411-T0-11
title: "RAGEN-2发现Agent RL训练存在推理崩溃问题"
title_en: "RAGEN-2 Uncovers Reasoning Collapse in Agent RL Training"
url: https://ai.daily.yangsir.net/daily/20260411-T0-11
issue_date: 2026-04-11
publish_date: 2026-04-10T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2604.06268
---

# RAGEN-2发现Agent RL训练存在推理崩溃问题

研究团队发布RAGEN-2论文，发现多轮LLM Agent的强化学习训练存在严重的推理稳定性问题。研究表明，现有依赖熵值稳定性的方法无法准确衡量推理质量下降。RAGEN-2通过引入推理路径分析，揭示了RL训练中推理崩溃的机制。该发现在需要复杂推理的Agent系统中尤为重要，可能导致任务性能大幅下降。研究人员建议在Agent训练中增加推理质量监控环节。

## English Version

**RAGEN-2 Uncovers Reasoning Collapse in Agent RL Training**

Researchers released RAGEN-2, revealing severe reasoning instability in multi-turn LLM Agent RL training. The study shows entropy-based stability metrics fail to detect reasoning quality drops. RAGEN-2 introduces path analysis to identify reasoning collapse mechanisms, with significant implications for complex reasoning tasks.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2604.06268)

**详情页**：https://ai.daily.yangsir.net/daily/20260411-T0-11

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