PA-OPSD: Privileged-Anchor On-Policy Self-Distillation for Mitigating Hallucinations in Multimodal Large Language Models

On-policy self-distillation (OPSD) has recently emerged as an effective approach for improving multimodal large language models (MLLMs) initialized from pretrained models. However, existing methods primarily enhance teacher predictions with privileged information or create teacher–student input discrepancies through image degradation, neither of which ensures that the student inherits the teacher’s ability to identify and exploit critical visual evidence. To address this limitation, we propose PA-OPSD (Privileged-Anchor On-Policy Self-Distillation), an on-policy self-distillation framework guided by privileged visual anchors. Specifically, the teacher efficiently aggregates image features conditioned on the question prompt, ground-truth answer, and an initial response prefix to extract privileged visual anchors relevant to the current generation process. These anchors then support two complementary supervision pathways. First, in the contrastive decoding pathway, the teacher compares token distributions obtained from the original image and a perturbed image in which the privileged visual anchors are masked. The resulting distributional divergence reveals hallucination-prone token positions whose predictions are sensitive to critical visual evidence, enabling more precise supervision of the student’s on-policy rollouts. Second, in the visual attention distillation pathway, the teacher transfers its attention over critical visual regions, informed by privileged information, to the student, guiding the student to ground its generation in the appropriate visual evidence. Extensive experiments demonstrate that PA-OPSD consistently improves multimodal reasoning while mitigating visual hallucinations, validating the effectiveness of privileged visual anchors and the proposed dual-path supervision mechanism. Code is available in supplementary material.