# Vision Reliance Gap Gate

In video reasoning the correct answer often hinges on a few visually decisive positions, yet post-training methods such as GRPO assign one coarse score to the whole reasoning trajectory and need multiple rollouts or external judges. VIOS instead has the model audit its own visual reliance: it compares next-token distributions with and without the video, takes the per-token reliance gap between the student and an answer-privileged teacher, and concentrates supervision where the student under-uses visual evidence. Across 12 video-understanding benchmarks, Qwen2.5-VL-7B-SFT rises from 46.35 to 49.95 on average, InternVL2.5-4B and InternVL2.5-8B gain 4.63 and 3.93 points, and training needs a single on-policy rollout with no external reward or judge.

In video reasoning the correct answer often hinges on a few visually decisive positions, yet post-training methods such as GRPO assign one coarse score to the whole reasoning trajectory and need multiple rollouts or external judges. VIOS instead has the model audit its own visual reliance: it compares next-token distributions with and without the video, takes the per-token reliance gap between the student and an answer-privileged teacher, and concentrates supervision where the student under-uses visual evidence. Across 12 video-understanding benchmarks, Qwen2.5-VL-7B-SFT rises from 46.35 to 49.95 on average, InternVL2.5-4B and InternVL2.5-8B gain 4.63 and 3.93 points, and training needs a single on-policy rollout with no external reward or judge.