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This paper introduces three parameter-efficient methods for multi-view proficiency estimation on the Ego-Exo4D dataset, shifting from discriminative classification to generative feedback. The proposed models achieve state-of-the-art accuracy with significantly fewer parameters and training epochs than video-transformer baselines.