InfiGUI-G1: Advancing GUI Grounding with Adaptive Exploration Policy
  Optimization
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.
689945f3f022d141f5d434a5
Yuhang Liu
claimed_verified
2025-08-11T06:52:51.989Z
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/avatars/bb1f8f2f2665944930cb5a7ce19c47d4.svg
Yuhang Liu
user
SiriusL
689945f3f022d141f5d434a6
Zeyu Liu
689945f3f022d141f5d434a7
Shuanghe Zhu
689945f3f022d141f5d434a8
Pengxiang Li
claimed_verified
2025-09-26T12:24:20.486Z
64245f2c089d5fae56b4549a
https://cdn-avatars.huggingface.co/v1/production/uploads/64245f2c089d5fae56b4549a/qUHFsL9Svwyj5BKpfMtaY.jpeg
Pengxiang Li
user
pengxiang
689945f3f022d141f5d434a9
Congkai Xie
claimed_verified
2025-08-13T07:27:35.776Z
6719f1ad725123d503b5ef3c
https://cdn-avatars.huggingface.co/v1/production/uploads/6719f1ad725123d503b5ef3c/P7yYxNVROsKPOVsqMu0jp.jpeg
Congkai Xie
user
congkai
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Jiasheng Wang
689945f3f022d141f5d434ab
Xueyu Hu
689945f3f022d141f5d434ac
Xiaotian Han
claimed_verified
2025-08-11T06:48:38.769Z
650dde4ce14eeb01d42b37a1
https://cdn-avatars.huggingface.co/v1/production/uploads/650dde4ce14eeb01d42b37a1/n5Yv24uofZ2XJjXdYCrKd.png
Xiaotian Han
user
xiaotianhan
689945f3f022d141f5d434ad
Jianbo Yuan
689945f3f022d141f5d434ae
Xinyao Wang
689945f3f022d141f5d434af
Shengyu Zhang
689945f3f022d141f5d434b0
Hongxia Yang
689945f3f022d141f5d434b1
Fei Wu
InfiGUI-G1: Advancing GUI Grounding with Adaptive Exploration Policy
  Optimization
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.
Adaptive Exploration Policy Optimization (AEPO) enhances semantic alignment in Multimodal Large Language Models (MLLMs) for GUI interaction, improving performance on benchmarks by up to 9.0% compared to RLVR.
Multimodal Large Language Models GUIs Reinforcement Learning with Verifiable Rewards Adaptive Exploration Policy Optimization multi-answer generation Adaptive Exploration Reward efficiency eta=U/C InfiGUI-G1-3B InfiGUI-G1-7B