LongVPO: From Anchored Cues to Self-Reasoning for Long-Form Video Preference Optimization

We present LongVPO, a novel two stage Direct Preference Optimization framework that enables short context vision language models to robustly understand ultra long videos without any long video annotations. In Stage 1, we synthesize preference triples by anchoring questions to individual short clips, interleaving them with distractors, and applying visual similarity and question specificity filtering to mitigate positional bias and ensure unambiguous supervision.