Large vision-language models (LVLMs) remain prone to hallucination. While recent visual Direct Preference Optimization (DPO) methods try to fix this by contrasting positive images with heuristic or offline negatives for the same correct response. We show that such negatives are mostly "trivial": the model already assigns near-zero probability to the correct response, so the resulting gradient is uninformative or even spuriously misdirected. We propose OViP, an on-policy framework that synthesizes negative images on the fly from the model’s own hallucinated responses via a diffusion prior, producing semantically misaligned yet still high-likelihood negatives. Across five hallucination and four general benchmarks, OViP achieves better hallucination mitigation on LLaVA-1.5 series and Qwen2.5-VL-3B, while being 2 times more compute-efficient than SIMA and incurring no degradation on general capabilities.