Qwen released QwenGyre - RL framework for training AI agents on extremely long complex tasks

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