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Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning
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Graphical User Interface (GUI) agents have made substantial strides in understanding and executing user instructions across diverse platforms. Yet, grounding these instructions to precise interface elements remains challenging, especially in complex, high-resolution, professional environments. Traditional supervised finetuning (SFT) methods often require large volumes of diverse data and exhibit weak generalization. To overcome these limitations, we introduce a reinforcement learning (RL) based framework that incorporates three core strategies: (1) seed data curation to ensure high quality training samples, (2) a dense policy gradient that provides continuous feedback based on prediction accuracy, and (3) a self evolutionary reinforcement finetuning mechanism that iteratively refines the model using attention maps. With only 3k training samples, our 7B-parameter model achieves state-of-the-art results among similarly sized models on three grounding benchmarks. Notably, it attains 47.3\% accuracy on the ScreenSpot-Pro dataset, outperforming much larger models, such as UI-TARS-72B, by a margin of 24.2\%. These findings underscore the effectiveness of RL-based approaches in enhancing GUI agent performance, particularly in high-resolution, complex environments.
Forward citations
Cited by 12 Pith papers
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GroundCUA, a 3.56M-element human-annotated desktop grounding dataset, and GroundNext models achieve strong UI grounding with less than one-tenth the SFT data of prior work.
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Learning Active Perception via Self-Evolving Preference Optimization for GUI Grounding
LASER uses self-evolving preference optimization to teach GUI models to crop and zoom before clicking, reaching 55.7% on ScreenSpot-Pro with a 7B model.
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UItron, trained with multi-stage SFT and curriculum RL on a new 1M-step Chinese app dataset, beats prior GUI agents on perception, grounding, planning, and especially Chinese mobile app scenarios.
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A real-device-centric foundation GUI agent with hybrid GUI+CLI batched actions, AutoResearch data flywheel, online RL, and a proactive harness reaches SOTA mobile and competitive desktop/web scores.
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HyperClick trains GUI grounding models with GRPO to output clicks plus confidence scores, jointly rewarding correct clicks and Brier-calibrated confidence, and reports SOTA accuracy on six of seven benchmarks with bet...
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A multi-agent RL workflow that interleaves single-agent updates, applied to mobile GUI control, achieves SOTA zero-shot performance and a +14.8 MATH500 gain.
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GUI-G$^2$: Gaussian Reward Modeling for GUI Grounding
Modeling GUI elements as Gaussian distributions instead of binary targets yields 92.0% (ScreenSpot), 93.3% (ScreenSpot-v2), and 47.5% (ScreenSpot-Pro) for a 7B model, outperforming UI-TARS-72B by a relative 24.7% on t...
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Separating text and icon grounding with iterative zooming improves GUI-element localization accuracy of existing vision-language models without retraining.
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