{"ID":2854817,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.14969","arxiv_id":"2510.14969","title":"LLMs as Scalable, General-Purpose Simulators For Evolving Digital Agent Training","abstract":"Digital agents require diverse, large-scale UI trajectories to generalize across real-world tasks, yet collecting such data is prohibitively expensive in both human annotation, infra and engineering perspectives. To this end, we introduce $\\textbf{UI-Simulator}$, a scalable paradigm that generates structured UI states and transitions to synthesize training trajectories at scale. Our paradigm integrates a digital world simulator for diverse UI states, a guided rollout process for coherent exploration, and a trajectory wrapper that produces high-quality and diverse trajectories for agent training. We further propose $\\textbf{UI-Simulator-Grow}$, a targeted scaling strategy that enables more rapid and data-efficient scaling by prioritizing high-impact tasks and synthesizes informative trajectory variants. Experiments on WebArena and AndroidWorld show that UI-Simulator rivals or surpasses open-source agents trained on real UIs with significantly better robustness, despite using weaker teacher models. Moreover, UI-Simulator-Grow matches the performance of Llama-3-70B-Instruct using only Llama-3-8B-Instruct as the base model, highlighting the potential of targeted synthesis scaling paradigm to continuously and efficiently enhance the digital agents.","short_abstract":"Digital agents require diverse, large-scale UI trajectories to generalize across real-world tasks, yet collecting such data is prohibitively expensive in both human annotation, infra and engineering perspectives. To this end, we introduce $\\textbf{UI-Simulator}$, a scalable paradigm that generates structured UI states...","url_abs":"https://arxiv.org/abs/2510.14969","url_pdf":"https://arxiv.org/pdf/2510.14969v1","authors":"[\"Yiming Wang\",\"Da Yin\",\"Yuedong Cui\",\"Ruichen Zheng\",\"Zhiqian Li\",\"Zongyu Lin\",\"Di Wu\",\"Xueqing Wu\",\"Chenchen Ye\",\"Yu Zhou\",\"Kai-Wei Chang\"]","published":"2025-10-16T17:59:38Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"LoRA\"]","has_code":false}