Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning

TL;DR

This paper presents a reinforcement learning framework that significantly improves GUI agent grounding accuracy in complex environments using minimal training data, outperforming larger models.

Contribution

Introduces a novel RL-based training method with self-evolutionary finetuning for GUI agents, reducing data needs and boosting performance.

Findings

Achieves 47.3% accuracy on ScreenSpot-Pro with only 3k samples.

Outperforms larger models like UI-TARS-72B by 24.2%.

Demonstrates effectiveness in high-resolution, complex GUI environments.

Abstract

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.