VisionCoach: Reinforcing Grounded Video Reasoning via Visual-Perception Prompting

Video reasoning requires models to locate and track question-relevant evidence across frames. While reinforcement learning (RL) withverifiable rewards improves accuracy, it still struggles to achieve reliablespatio-temporal grounding during the reasoning process. Moreover, im-proving grounding typically relies on scaled training data or inference-timeperception tools, which increases annotation cost or computational cost.To address this challenge, we propose VisionCoach, an input-adaptiveRL framework that improves spatio-temporal grounding through visualprompting as training-time guidance. During RL training, visual promptsare selectively applied to challenging inputs to amplify question-relevantevidence and suppress distractors. The model then internalizes theseimprovements through self-distillation, enabling grounded reasoning di-rectly on raw videos without visual prompting at inference. VisionCoachconsists of two components: (1) Visual Prompt Selector, which predictsappropriate prompt types conditioned on the video and question, and(2) Spatio-Temporal Reasoner, optimized with RL under visual promptguidance and object-aware grounding rewards that enforce object identityconsistency and multi-region bounding-box IoU. Extensive experimentsdemonstrate that VisionCoach achieves state-of-the-art performanceacross diverse video reasoning, video understanding, and temporal ground-ing benchmarks (V-STAR, VideoMME, World-Sense, VideoMMMU, Per-ceptionTest, and Charades-STA), while maintaining a single efficientinference pathway without external tools. Our results highlight the effec-tiveness of training-time visual prompting as a lightweight mechanismfor improving grounded video reasoning.