LLMs as Scalable, General-Purpose Simulators For Evolving Digital Agent Training

Venue: iclr2026 (Reject) Authors: OpenReview: https://openreview.net/forum?id=762D8ndh2E

Relevance

LLM score: 1/3 — The paper addresses data-efficient scaling for agent training via synthetic data, tangential to the group's focus on energy-efficient training techniques like sparsity or quantization. Keyword hits: data-efficient

TLDR

(none provided)

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 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.

Keywords

simulator, digital agent, world model, evolving training