Qwen-Planner-Agent: A Closed-Loop AI-for-AI Framework for Real-World Mobile Planner Agents
Qwen-Planner-Agent uses a closed-loop AI-for-AI pipeline and leads MobilePA-Bench for mobile planning.
Qwen-Planner-Agent is developed in a closed-loop AI-for-AI framework aimed at long-horizon mobile planning, where real-device interaction is costly. Specialized agents generate tasks and trajectories and use training feedback to steer later data, while training combines a supervised planning cold start with hybrid-environment online reinforcement learning and Competence-Aware Reward-and-Advantage Engineering (CARE). An execution loop co-evolves memory, skills, and tools and feeds failure traces back into model and harness updates. The system is reported best overall on MobilePA-Bench, improving tool use, memory, skills, and sub-agent coordination over its base model, with gains on non-mobile agent benchmarks and general capabilities largely preserved.
- A shared action-feedback-verification contract links data, training, and deployment.
- Agents build tasks, collect trajectories, and curate data under human gating.
- CARE reduces reasoning and tool-use cost while preserving task performance.
- It leads MobilePA-Bench and improves non-mobile agent benchmarks over its base model.
- AI models
- Qwen-Planner-Agent
From the original article
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