Tingyu Qu

The work builds Qwen-Planner-Agent within a closed-loop AI-for-AI framework that links data production, model training, and deployment through a shared action-feedback-verification contract: AI for Data builds a human-gated agentic data flywheel, AI for Training combines a supervised planning cold start with hybrid-environment online agentic reinforcement learning and introduces CARE to reduce reasoning and tool-use costs, and AI drives model-harness co-evolution; the agent achieves the best overall performance among all evaluated models and systems on MobilePA-Bench, improving over its base model across tool use, memory, skills, and sub-agent coordination, with further gains on non-mobile agentic benchmarks while largely preserving general capabilities.

The work builds Qwen-Planner-Agent within a closed-loop AI-for-AI framework that links data production, model training, and deployment through a shared action-feedback-verification contract: AI for Data builds a human-gated agentic data flywheel, AI for Training combines a supervised planning cold start with hybrid-environment online agentic reinforcement learning and introduces CARE to reduce reasoning and tool-use costs, and AI drives model-harness co-evolution; the agent achieves the best overall performance among all evaluated models and systems on MobilePA-Bench, improving over its base model across tool use, memory, skills, and sub-agent coordination, with further gains on non-mobile agentic benchmarks while largely preserving general capabilities.