When training vision-language models with self-distillation, you need to verify that token-level corrections actually come from visual reasoning, not linguistic artifacts—VAG does this by checking if corrections persist when visual evidence is degraded.
This paper addresses a key problem in multimodal reinforcement learning: when a teacher model provides token-level corrections to improve visual reasoning, these corrections may be contaminated by non-visual factors like language patterns or the teacher's perspective.