VEM: Environment-Free Exploration for Training GUI Agent with Value
  Environment Model
Authors: name: Jiani Zheng, name: Lu Wang, name: Fangkai Yang, name: Chaoyun Zhang, name: Lingrui Mei, name: Wenjie Yin, name: Qingwei Lin, name: Dongmei Zhang, name: Saravan Rajmohan, name: Qi Zhang

Training Vision-Language Models (VLMs) for Graphical User Interfaces (GUI)
agents via Reinforcement Learning (RL) faces critical challenges:
environment-based RL requires costly interactions, while environment-free
methods struggle with distribution shift and reward generalization. We propose
an environment-free RL framework that decouples value estimation from policy
optimization by leveraging a pretrained Value Environment Model (VEM). VEM
predicts state-action values directly from offline data, distilling human-like
priors about GUI interaction outcomes without requiring next-state prediction
or environmental feedback. This avoids compounding errors and enhances
resilience to UI changes by focusing on semantic reasoning (e.g., Does this
action advance the user's goal?). The framework operates in two stages: (1)
pretraining VEM to estimate long-term action utilities and (2) guiding policy
exploration with frozen VEM signals, enabling layout-agnostic GUI automation.
Evaluated on Android-in-the-Wild benchmarks, VEM achieves state-of-the-art
performance in both offline and online settings, outperforming environment-free
baselines significantly and matching environment-based approaches without
interaction costs. Importantly, VEM demonstrates that semantic-aware value
estimation can achieve comparable performance with online-trained methods.