{"ID":2921729,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-03T05:56:00.181519634Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01230","arxiv_id":"2606.01230","title":"HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation","abstract":"Large language model agents are moving beyond text-only interaction toward physical-world control, with smart homes as a representative domain. Real domestic interaction requires understanding ambiguous intents, operating in dynamic environments, and performing multi-turn reasoning. However, existing methods struggle to generate high-quality training data for smart home agents. We propose HomeFlow, a verifiable data flywheel for this domain. HomeFlow uses HomeEnv as a unified simulation environment and HomeMaker to procedurally generate diverse home settings. Subsequently, Blueprint compiles open-ended user intents into executable state-based success conditions, while MCTS-Flow synthesizes diverse, verifiable multi-turn trajectories through environment-guided tree search. We then optimize the agents via supervised fine-tuning and step-wise RLVE, which facilitates iterative improvement through authentic physical feedback. We further construct SmartHome-Bench to evaluate the agent across various smart home tasks. On this benchmark, HomeFlow-RL-4B and HomeFlow-RL-8B achieve task success rates of 84.60% and 87.03%. It is worth noting that HomeFlow-RL-8B even surpasses the leading GPT-5.5 by 1.23 percentage points.","short_abstract":"Large language model agents are moving beyond text-only interaction toward physical-world control, with smart homes as a representative domain. Real domestic interaction requires understanding ambiguous intents, operating in dynamic environments, and performing multi-turn reasoning. However, existing methods struggle t...","url_abs":"https://arxiv.org/abs/2606.01230","url_pdf":"https://arxiv.org/pdf/2606.01230v1","authors":"[\"Yi Gu\",\"Huacan Wang\",\"Shuo Zhang\",\"Yuqing Hou\",\"Lei Xue\",\"Weipeng Ming\",\"Chen Liu\",\"Fangzhou Yu\",\"Kuan Li\",\"Ronghao Chen\",\"Sen Hu\",\"Xiaofeng Mou\",\"Yi Xu\"]","published":"2026-05-31T13:26:46Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}