ViB-OPD: Visual Evidence Mining and Boundary-Balanced Video Distillation

Existing approaches commonly use supervised fine-tuning (SFT) or reinforcement learning (RL) to develop video understanding and reasoning; however, SFT is constrained by the quality and coverage of fixed demonstrations, while RL often relies on sparse final-answer rewards that provide little direct guidance for intermediate reasoning. On-policy distillation (OPD) offers an alternative by providing teacher distributional supervision along trajectories generated by the current student. Yet applying OPD to video reasoning leaves two issues unresolved: teacher distributions can be limited by incomplete visual evidence, and token-wise aggregation can make supervision allocation depend on expression length. We propose ViB-OPD, comprising Visual Evidence Mining (VEM) and Boundary-Balanced OPD (BB-OPD). A stronger frozen teacher uses supplementary training-time visual context to guide the student without expanding its inference input. VEM combines global observation, question-conditioned localization, and optional local reinspection to organize candidate evidence into a separate contact sheet, enriching the teacher's visual basis. BB-OPD initializes semantic groups and combines local boundary perturbation with within-segment averaging and equal segment weights to reduce the influence of expression length and a single segmentation on supervision allocation. It preserves the original student trajectory and token-level distillation objective. Evaluation on six public benchmarks and across model families demonstrates training gains.