aiqbench analysis
Alibaba's QwenGyre represents a significant advancement in training coding agents for extreme-long-horizon tasks, utilizing an elastic reinforcement learning framework to handle rollouts of up to one million tokens without modifying the underlying harness. This development directly addresses scalability challenges in agent-based systems by optimizing resource efficiency during extended inference periods.
Alibaba Qwen Introduces QwenGyre: Elastic Reinforcement Learning RL for XLong-Horizon Agents
Alibaba’s Qwen team has introduced QwenGyre, an elastic reinforcement learning RL framework for extreme-long-horizon agent training. QwenGyre is an online reinforcement learning system from Alibaba that trains coding agents through unmodified, black-box harnesses. It targets rollouts that run for hours and process close to 1M tokens. The QwenGyre elastic RL framework was used on Qwen’s […] The post Alibaba Qwen Introduces QwenGyre: Elastic Reinforcement Learning RL for XLong-Horizon Agents appeared first on MarkTechPost.
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