LLMs as Scalable, General-Purpose Simulators For Evolving Digital Agent
  Training
Authors: name: Yiming Wang, name: Da Yin, name: Yuedong Cui, name: Ruichen Zheng, name: Zhiqian Li, name: Zongyu Lin, name: Di Wu, name: Xueqing Wu, name: Chenchen Ye, name: Yu Zhou, name: Kai-Wei Chang
Organization: UCLA NLP

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 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 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.