Qwen(@Alibaba_Qwen)

Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. ✨ It laun...

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Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. ✨ It laun...

TL;DR · AI 摘要

QwenGyre通过弹性调度和轨迹处理技术，使长期强化学习训练效率提升44%，并支持超大规模模型训练。

核心要点

  • Elastic Scheduler实现1.85倍于Colocate的训练加速
  • Trajectory Processor处理700K tokens/rollout的超大规模轨迹
  • NL2RepoBench基准测试48步内准确率提升6%

结构提纲

按章节快速跳转。

  1. §技术发布

    介绍QwenGyre系统及其在长期强化学习中的应用价值

  2. ·弹性调度机制

    动态分配GPU资源实现训练与执行无缝衔接

  3. ·轨迹处理架构

    通过前缀共享树结构优化分支执行轨迹处理

  4. ›性能验证

    在Qwen3.8-2.4T-A95B模型上实现44%训练时间缩减

  5. §技术影响

    推动极端长期任务训练效率突破性提升

思维导图

用一张图看清主题之间的关系。

查看大纲文本（无障碍 / 无 JS 友好）
  • QwenGyre系统
    • 核心设计
      • Elastic Scheduler
        • 动态GPU分配
      • Trajectory Processor
        • 轨迹树优化
    • 性能指标
      • 1.85×加速
      • 44%时间缩减

金句 / Highlights

值得收藏与分享的关键句。

#强化学习#QwenGyre#阿里巴巴#XLong
打开原文

Qwen on X: "Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. ✨ It launches with two core designs: 🥳 - Elastic Scheduler: reallocates GPUs between rollout & training as demand shifts—live executions never interrupted. Up to 1.85× speedup over Colocate, 1.78… / X

Qwen

@Alibaba_Qwen

Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. ✨ It launches with two core designs: 🥳 - Elastic Scheduler: reallocates GPUs between rollout & training as demand shifts—live executions never interrupted. Up to 1.85× speedup over Colocate, 1.78× over Async. - Trajectory Processor: turns branching executions (compaction, sub-agents, retries) into prefix-shared trajectory trees with bounded, deduplicated training samples. Partial progress after timeout still gets scored. Scaled to Qwen3.8-2.4T-A95B with ~700K tokens per rollout, QwenGyre lifts NL2RepoBench from 52.5% to 58.5% in 48 steps, cutting end-to-end training time by ~44% vs Async. 🔗 Read the paper:

arxiv.org/abs/2609.33848

1:11 PM · Oct 12, 2026

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