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Applicability of VPPO to subjective or creative multimodal tasks

Determine whether the Visually-Perceptive Policy Optimization (VPPO) algorithm is applicable to subjective or creative multimodal tasks, including detailed image captioning and visual storytelling, where the notion of a single visually-grounded reasoning chain is less clearly defined.

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Background

The paper introduces Visually-Perceptive Policy Optimization (VPPO), a reinforcement learning algorithm for Large Vision-LLMs that leverages token-level visual dependency to shape trajectory advantages and filter token-level gradients. VPPO shows strong performance on reasoning-centric benchmarks across 7B and 32B model scales.

In the limitations, the authors note that while VPPO is effective on reasoning-intensive tasks (math, geometry, logic), many multimodal tasks are subjective or creative and may not follow a single, clearly defined visually-grounded reasoning chain. The authors explicitly state uncertainty about whether VPPO’s reliance on such a chain transfers to tasks like detailed image captioning or visual storytelling.

References

Its applicability to more subjective or creative tasks, such as detailed image captioning or visual storytelling, where the notion of a single ``visually-grounded'' reasoning chain is less clear, remains an open question.

— Spotlight on Token Perception for Multimodal Reinforcement Learning (2510.09285 - Huang et al., 10 Oct 2025) in Appendix, Section "Limitations" (Scope of Generalization paragraph)