Qwen-Planner-Agent: a closed-loop AI-for-AI framework for mobile planner agents
Tongyi-MAI · hf · 2026-09-25
Tongyi-MAI built Qwen-Planner-Agent within a closed-loop AI-for-AI framework, exploring whether AI can be both the object of development and an active participant in building next-gen AI systems, tested on demanding long-horizon mobile planning. A shared action-feedback-verification contract connects data production, training, and deployment.
Three loops: (i) AI for Data — a human-gated agentic data flywheel where specialized agents build tasks, collect trajectories, and curate data guided by training feedback; (ii) AI for Training — supervised planning cold start plus hybrid-environment online agentic RL with CARE (Competence-Aware Reward-and-Advantage Engineering) to cut reasoning/tool-use cost while preserving performance; (iii) model–harness co-evolution via an execution-evidence loop orchestrating memory, skills, and tools at runtime.
Qwen-Planner-Agent achieves the best overall score on MobilePA-Bench, beating its base model on tool use, memory, skills, and sub-agent coordination, while improving on non-mobile agentic benchmarks and preserving general capabilities.
Related event: Qwen Unveils Mobile Agent Suite and Open Benchmark(2 posts)→
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