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

arXiv:2609.29892 · cs.AI · Submitted 2026-09-24 · Read on arXiv

cs.AI

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/sierra-research/tau2-bench

Project page: https://tongyi-mai.github.io/Qwen-Planner-Agent

Terminology

• Reinforcement Learning — 27× in this paper · explained in 4 episodes
• Benchmark — 19× in this paper · explained in 2 episodes
• Trace — 17× in this paper · explained in 2 episodes
• Calibration — 16× in this paper · explained in 7 episodes
• Verification — 15× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Verifier — 10× in this paper · explained in 2 episodes
• Adaptation — 9× in this paper · explained in 1 episode
• Benchmarking — 5× in this paper · explained in 4 episodes
• Competence-Aware Reward-and-Advantage Engineering — 5× in this paper
• Grounding — 5× in this paper · explained in 3 episodes
• Provenance — 5× in this paper · explained in 4 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Ablation Studies — 4× in this paper · explained in 1 episode
• Aggregation — 4× in this paper · explained in 1 episode
• Task Decomposition — 4× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Online Reinforcement Learning — 3× in this paper · explained in 2 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Agentic Optimization — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• GRPO — 2× in this paper · explained in 6 episodes
• Model Context Protocol — 2× in this paper
• AI Agents — 1× in this paper · explained in 2 episodes
• Agentic System — 1× in this paper · explained in 2 episodes
• Agentic Task — 1× in this paper · explained in 1 episode
• Agentic Tasks — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Mixture-of-Experts (MoE) Models — 1× in this paper · explained in 3 episodes
• Model Context Protocol (MCP) — 1× in this paper · explained in 8 episodes
• On-Policy Distillation — 1× in this paper · explained in 3 episodes
• Procedural Knowledge — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Voyager: An Open-Ended Embodied Agent with Large Language Models
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization
• Proximal Policy Optimization Algorithms
• MobilePA-Bench: Benchmarking Mobile Planner Agents on Complex Real-World Tasks
• GLM-5: from Vibe Coding to Agentic Engineering
• Kimi K3: Open Frontier Intelligence
• LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
• Beyond a Million Tokens: Benchmarking and Enhancing Long-Term Memory in LLMs
• Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents
• MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers
• The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution
• SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
• Are We Done with MMLU?
• C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models
• Instruction-Following Evaluation for Large Language Models
• TextGrad: Automatic "Differentiation" via Text
• DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
• Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks
• The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

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