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Qwen-Planner-Agent: A Closed-Loop AI-for-AI Framework for Real-World Mobile Planner Agents

Research LLM Agents

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Content 90
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Representative image for Qwen-Planner-Agent: A Closed-Loop AI-for-AI Framework for Real-World Mobile Planner Agents

Merged summary

TL;DR - Qwen-Planner-Agent is a closed-loop framework that uses specialized AI agents to improve mobile planner agents across data generation, training, and deployment. It leads evaluated systems on MobilePA-Bench while also improving performance on non-mobile agent benchmarks without substantially degrading general capabilities.

  • A shared action-feedback-verification contract connects data production, model training, and runtime deployment.
  • A human-gated data flywheel creates tasks, gathers interaction trajectories, curates training data, and adapts future generation using training feedback.
  • Training combines supervised planning initialization with online reinforcement learning across hybrid environments.
  • Its CARE method reduces reasoning and tool-use costs while preserving task performance; execution evidence and failure traces support joint model–harness improvement.

Sources (1)

Qwen-Planner-Agent: A Closed-Loop AI-for-AI Framework for Real-World Mobile Planner Agents

arXiv cs.AI Tingyu Qu, Weigao Sun, Yuecheng Liu, Yucheng Zhao, Yi Zhu, Yifeng Ding, Qiyi Wang, Sihan Cao, Pengkun Jiao, Hanlei Xie, Xiongwei Wu, Qichao Wang, Haodong Zhang, Jiajun Liu, Yuhao Wang, Yuqing Xie, Junpeng Zhao, Long Chen, Ming Ma, Sihan Yang, Ziwang Zhao, Yanhao Jia, Liangquan Gong, Feida Zhu, Yiran Zhong, Steven Hoi 2026-09-24 arXiv:2609.29892
Public signals Hugging Face upvotes 10
Providers: Hugging Face · Upvotes 10 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-26 14:03:48.988658 UTC

TL;DR - Qwen-Planner-Agent is a closed-loop framework that uses specialized AI agents to improve mobile planner agents across data generation, training, and deployment. It leads evaluated systems on MobilePA-Bench while also improving performance on non-mobile agent benchmarks without substantially degrading general capabilities.

  • A shared action-feedback-verification contract connects data production, model training, and runtime deployment.
  • A human-gated data flywheel creates tasks, gathers interaction trajectories, curates training data, and adapts future generation using training feedback.
  • Training combines supervised planning initialization with online reinforcement learning across hybrid environments.
  • Its CARE method reduces reasoning and tool-use costs while preserving task performance; execution evidence and failure traces support joint model–harness improvement.
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