Qwen3.6-122B

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   Alibaba Qwen releases QwenGyre, an online RL framework for long-horizon agent tasks

   Alibaba's Qwen team released QwenGyre, an online reinforcement learning framework aimed at long-horizon agent tasks, on October 12. According to the company, such tasks can run for hours, involve hundreds of interdependent interactions with the environment and process millions of tokens; QwenGyre splits GPUs into compute units so that units freed when a batch of tasks finishes roll into training while running tasks keep their progress and caches, and it uses Token-in Token-out recording to organize shared-prefix trajectories into a tree, expanding only selected paths as training samples while Partial Scoring salvages feedback from timed-out but partially completed tasks. In the reported NL2RepoBench online RL experiment, with roughly 700,000 tokens per rollout, pass rate rose from 52.5% to 58.5% after 48 training steps, and QwenGyre delivered a 1.78x end-to-end speedup over an Async implementation at the same GPU budget, with cross-task end-to-end speedups on Qwen3.6-122B across NL2RepoBench, DeepSWE and TerminalBench.

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