arXiv:2604.09349v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has significantly advanced the reasoning ability of vision-language models (VLMs). However, the inherent text-dominated nature of VLMs often leads to insufficient visual faithfulness, characterized by sparse attention activation to visual tokens. More importantly, our empirical analysis reveals that temporal visual forgetting along reasoning steps exacerbates this deficiency. To bridge this gap, we propose Visually-Guided Policy Optimization (VGPO), a novel framework to reinforce vis
The continuous advancements in reinforcement learning and the increasing demand for more robust vision-language models drive the urgency for solutions addressing visual faithfulness and temporal forgetting.
Improving the visual reasoning capabilities of AI is critical for developing more reliable and effective autonomous systems, impacting industries from robotics to advanced analytics.
This research introduces a novel framework that directly addresses key limitations in how AI models process and retain visual information during complex reasoning tasks, leading to more visually faithful outcomes.
- · AI developers
- · Robotics industry
- · Vision-language model researchers
- · Autonomous systems
- · Developers reliant on text-dominated VLM approaches
- · Systems with poor visual attention mechanisms
Visually-Guided Policy Optimization (VGPO) significantly enhances the ability of vision-language models to integrate and retain visual information.
This advancement could lead to AI agents with superior situational awareness and more nuanced understanding of physical environments.
The improved visual fidelity may accelerate the development and deployment of truly general-purpose AI agents in complex real-world scenarios.
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Read at arXiv cs.AI