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GuirlVG: Incentivize GUI Visual Grounding via Empirical Exploration on Reinforcement Learning
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GuirlVG: Incentivize GUI Visual Grounding via Empirical Exploration on Reinforcement Learning
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Graphical user interface visual grounding (GUI-VG), a core capability for GUI agents, has primarily relied on supervised fine-tuning (SFT) of multimodal large language models (MLLMs), which demands extensive data curation and significant training costs. However, as MLLMs continue to advance and even cover GUI domains during pretraining, the necessity of exhaustive SFT post-training becomes increasingly questionable. Meanwhile, recent successes of rule-based reinforcement fine-tuning (RFT) suggest a more efficient alternative. Despite this promise, the optimal manner of applying RFT for GUI-VG remains unexplored. To bridge this gap, we introduce GuirlVG, a reinforcement learning-based GUI-VG method built on a systematic empirical study and a novel stabilization technique. We find that naive application of RFT underperforms the SFT baseline, motivating a deeper exploration. First, we decompose RFT into its core components and analyze the optimal formulation of each. Second, we propose a novel Adversarial KL Factor that dynamically stabilizes training to mitigate reward over-optimization. Third, we further explore the training configurations of RFT to enhance effectiveness. Extensive experiments show that GuirlVG, with only 5.2K training samples, outperforms SFT methods trained on over 10M samples, achieving a 7.7% improvement on ScreenSpot, a 17.2% improvement on ScreenSpotPro, and 91.9% accuracy on ScreenSpotV2.
Forward citations
Cited by 6 Pith papers
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Learn where to Click from Yourself: On-Policy Self-Distillation for GUI Grounding
GUI-SD is the first on-policy self-distillation framework for GUI grounding that adds privileged bounding-box context and entropy-guided weighting to outperform GRPO methods on six benchmarks in accuracy and efficiency.
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Learn where to Click from Yourself: On-Policy Self-Distillation for GUI Grounding
GUI-SD introduces on-policy self-distillation with visually enriched privileged context and entropy-guided weighting, outperforming GRPO and naive OPSD on six GUI grounding benchmarks while improving training efficiency.
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GUI-Perturbed: Domain Randomization Reveals Systematic Brittleness in GUI Grounding Models
GUI-Perturbed shows that GUI grounding models suffer systematic accuracy collapse under relational instructions and visual changes such as 70% zoom, with even augmented fine-tuning worsening results.
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GUI-Perturbed: Domain Randomization Reveals Systematic Brittleness in GUI Grounding Models
Controlled visual and instruction perturbations expose large, systematic spatial-reasoning and scale brittleness in 7B GUI grounding models that standard fixed-scene benchmarks miss.
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One Forward Beats Two: InnerZoom for Accurate and Efficient GUI Grounding
InnerZoom bridges cross-layer evidence in one forward pass to achieve SOTA GUI grounding accuracy on six benchmarks while cutting latency up to 31.8% versus two-pass baselines.
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Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding
A co-evolving proposer-critic RL framework improves GUI grounding accuracy by letting the model critique its own proposals rendered on screenshots.
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