VisionCoach: Reinforcing Grounded Video Reasoning via Visual-Perception Prompting
Quick summary
arXiv:2603.14659v2 Announce Type: replace-cross Abstract: Video reasoning requires models to locate and track question-relevant evidence across frames. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning process. Moreover, improving grounding typically relies on scaled training data or inference-time perception tools, which increases annotation cost or computational cost. To address this challenge, we propose VisonCoach, an input-adaptive RL framework that improves spatio-temporal gro
Key takeaways
- arXiv:2603.14659v2 Announce Type: replace-cross Abstract: Video reasoning requires models to locate and track question-relevant evidence across frames.
- While reinforcement learning (RL) with verifiable rewards improves accuracy, it still struggles to achieve reliable spatio-temporal grounding during the reasoning process.
- Moreover, improving grounding typically relies on scaled training data or inference-time perception tools, which increases annotation cost or computational cost.
Why it matters
“VisionCoach: Reinforcing Grounded Video Reasoning via Visual-Perception Prompting” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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