🔥 More Thought, Less Accuracy? On the Dual Nature of Reasoning in Vision-Language Models

💡 This paper explores the concept of reasoning in Vision Language Models and identifies a dual nature of multimodal reasoning. While reasoning enhances logical inference and improves performance on complex tasks, it can also impair perceptual grounding, leading to recognition failures on basic visual questions. The authors attribute this phenomenon to visual forgetting, where prolonged reasoning causes the model to disregard visual input. To address this issue, the authors propose Vision Anchored Policy Optimization, a method that steers the reasoning process toward visually grounded trajectories. The resulting model, VAPO Thinker 7B, significantly strengthens the model's reliance on visual information and achieves state of the art results on a range of benchmarks. The key contribution of this paper is the identification of the dual nature of multimodal reasoning and the development of a method to balance reasoning and visual grounding, leading to improved performance on visual tasks.

📅 Published on Sep 30, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2509.25848
• PDF: https://arxiv.org/pdf/2509.25848
• Project Page: https://xytian1008.github.io/VAPO/

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📢 By: https://xn--r1a.website/PaperNexus

#VisionLanguageModels #MultimodalReasoning #VisualForgetting #VisionAnchoredPolicyOptimization #PerceptualGrounding