Alibaba Qwen Introduces QwenGyre RL Framework

Alibaba's Qwen team has introduced QwenGyre, an elastic reinforcement learning (RL) framework designed for training AI agents on extreme-long-horizon tasks. QwenGyre is an online reinforcement learning system that trains coding agents through unmodified, black-box harnesses, targeting rollouts that can run for hours and process close to 1 million tokens. The framework was tested on Alibaba's Qwen 3.6 122B and Qwen 3.8 2.4T models, with approximately 700,000 tokens per rollout. It operates on multi-node GPU clusters, with the 122B model utilizing 24 to 32 nodes and the 2.4T model using 192 nodes. The system employs two core ideas: it moves idle GPUs between rollout and training, and it transforms branching agent histories into bounded training samples. Performance tests showed QwenGyre to be 1.38x to 1.78x faster than Async and 1.21x to 1.85x faster than Colocate, achieving matched scores. The best performance was a 1.85x end-to-end speedup over Colocate on TerminalBench. QwenGyre addresses challenges in 'XLong-horizon' tasks, defined as agent runs spanning hours with hundreds of model and tool interactions, such as generating full repositories from specifications. Two primary problems at this scale are long rollout tails, where training waits for the slowest execution, and the complexity of branching agent histories, which inflate training costs by duplicating shared prefixes. The research team has not released public code for QwenGyre.