Guiding VLM Agents with Process Rewards at Inference Time for GUI Navigation
Keywords: Visual Language Model, Agent, GUI, Process Reward Model
TL;DR: We propose a method to guide VLM agents during GUI navigation using reward-based process supervision at inference time.
Abstract: Recent advancements in visual language models (VLMs) have notably enhanced their capabilities in handling complex Graphical User Interface (GUI) interaction tasks. Despite these improvements, current frameworks often struggle to generate correct actions in challenging GUI environments. State-of-the-art commercial VLMs are black-boxes, and fine-tuning open-source VLMs for GUI tasks requires significant resources. Additionally, existing trajectory-level evaluation and refinement techniques frequently fall short due to delayed feedback and local optimization issues. To address these challenges, we propose an approach that guides VLM agents with process supervision by a reward model during GUI navigation and control at inference time. This guidance allows the VLM agent to optimize actions at each inference step, thereby improving performance in both static and dynamic environments. In particular, our method demonstrates significant performance gains in the GUI navigation task setting, achieving a around 5\% improvement in action accuracy for static environments and a near 15\% increase in task success rate in dynamic environments. With further integration of trajectory reflection and retry mechanisms, we also demonstrate even greater enhancement in task success.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 4533
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