InfiGUI-G1: Advancing GUI Grounding with Adaptive Exploration Policy
  Optimization
Authors: name: Yuhang Liu, name: Zeyu Liu, name: Shuanghe Zhu, name: Pengxiang Li, name: Congkai Xie, name: Jiasheng Wang, name: Xueyu Hu, name: Xiaotian Han, name: Jianbo Yuan, name: Xinyao Wang, name: Shengyu Zhang, name: Hongxia Yang, name: Fei Wu

The emergence of Multimodal Large Language Models (MLLMs) has propelled the
development of autonomous agents that operate on Graphical User Interfaces
(GUIs) using pure visual input. A fundamental challenge is robustly grounding
natural language instructions. This requires a precise spatial alignment, which
accurately locates the coordinates of each element, and, more critically, a
correct semantic alignment, which matches the instructions to the functionally
appropriate UI element. Although Reinforcement Learning with Verifiable Rewards
(RLVR) has proven to be effective at improving spatial alignment for these
MLLMs, we find that inefficient exploration bottlenecks semantic alignment,
which prevent models from learning difficult semantic associations. To address
this exploration problem, we present Adaptive Exploration Policy Optimization
(AEPO), a new policy optimization framework. AEPO employs a multi-answer
generation strategy to enforce broader exploration, which is then guided by a
theoretically grounded Adaptive Exploration Reward (AER) function derived from
first principles of efficiency eta=U/C. Our AEPO-trained models, InfiGUI-G1-3B
and InfiGUI-G1-7B, establish new state-of-the-art results across multiple
challenging GUI grounding benchmarks, achieving significant relative
improvements of up to 9.0% against the naive RLVR baseline on benchmarks
designed to test generalization and semantic understanding. Resources are
available at https://github.com/InfiXAI/InfiGUI-G1.