UI-Ins: Enhancing GUI Grounding with Multi-Perspective
  Instruction-as-Reasoning
Authors: name: Liangyu Chen; name: Hanzhang Zhou; name: Chenglin Cai; name: Jianan Zhang; name: Panrong Tong; name: Quyu Kong; name: Xu Zhang; name: Chen Liu; name: Yuqi Liu; name: Wenxuan Wang; name: Yue Wang; name: Qin Jin; name: Steven Hoi
Published: 2025-10-23 07:18:32

Abstract: GUI grounding, which maps natural-language instructions to actionable UI
elements, is a core capability of GUI agents. Prior works largely treats
instructions as a static proxy for user intent, overlooking the impact of
instruction diversity and quality on grounding performance. Through a careful
investigation of existing grounding datasets, we find a 23.3% flaw rate in
their instructions and show that inference-time exploitation of instruction
diversity yields up to a substantial 76% relative performance improvement. In
this paper, we introduce the Instruction-as-Reasoning paradigm, treating
instructions as dynamic analytical pathways that offer distinct perspectives
and enabling the model to select the most effective pathway during reasoning.
To achieve this, we propose a two-stage training framework: supervised
fine-tuning (SFT) on synthesized, diverse instructions to instill
multi-perspective reasoning, followed by reinforcement learning (RL) to
optimize pathway selection and composition. Our resulting models, UI-Ins-7B and
UI-Ins-32B, achieve state-of-the-art results on five challenging grounding
benchmarks and exhibit emergent reasoning, selectively composing and
synthesizing novel instruction pathways at inference. In particular, UI-Ins-32B
attains the best grounding accuracy, scoring 87.3% on UI-I2E-Bench, 57.0% on
ScreenSpot-Pro, and 84.9% on MMBench-GUI L2. Furthermore, our model
demonstrates strong agentic potential, achieving a 74.1% success rate on
AndroidWorld using UI-Ins-7B as the executor. Our in-depth analysis reveals
additional insights such as how reasoning can be formulated to enhance rather
than hinder grounding performance, and how our method mitigates policy collapse
in the SFT+RL framework. All code and model checkpoints will be publicly
released in https://github.com/alibaba/UI-Ins.