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This paper introduces Super and Supra, sparse parameter-efficient fine-tuning methods that reuse pruning-inspired saliency signals (like Wanda scores) to select trainable supports, and combine them with LoRA to achieve high accuracy on arithmetic tasks with reduced memory and compute.