A new self-distillation framework, VISTA, is introduced to address Trajectory Deviation in deep learning models. This issue occurs when models converge to suboptimal solutions despite high validation accuracy. VISTA uses a validation-informed Marginal Coverage score to identify expert anchors that retain specialized competence and integrates them online during training to regularize the loss landscape and preserve mastered knowledge.