Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. โจ
It launches with two core designs: ๐ฅณ
- Elastic Scheduler: reallocates GPUs between rollout & training as demand shiftsโlive executions never interrupted. Up to 1.85ร speedup over Colocate, 1.78ร 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.
๐ Read the paper:
https://t.co/gbKPIiqosk
@JensenHuang@satyanadella RTX Spark, shouldโve been a Linux laptop with 256gb of ram, GB10 one PFLOP with QSFP port so that you can connect to DGX Spark to pool the ram.