arxivcs.LGcs.CL2026-07-10
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning
Ivan Ilin, Philip Zmushko, Peter Richtárik
Large language models (LLMs) remain expensive to fine-tune because full-parameter updates require substantial memory, compute, and per-task storage. We study whether saliency signals originally developed for pruning can be reused to choose where a model should adapt. We propose S…