通义千问发布 QwenGyre：面向超长程智能体训练的弹性 RL 框架

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通义千问发布 QwenGyre，将弹性 RL 引入超长程（XLong）智能体训练，首发两项核心设计：Elastic Scheduler 在 rollout 与训练之间动态重新分配 GPU 且不中断在线执行。

正文 · 原文

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