Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
Train What You Deploy: Closing the MLP Reachability Gap in Low-Rank Clone Distillation
Wenhui Chen, Zhifeng Li, Jie Zhou +5
A compressed student has two shapes that need not agree: the weight it deploys at inference and the weight family its training can reach. We show that a state-of-the-art weight-inh…
cs.LG2026
Interpretability-Guided Layer Selection over Subspace Projection: SAEs as Stethoscopes, Not Scalpels, for Raw Task Vector Model Editing
Li Lei, Madalina Ciobanu, Qingqing Mao +1
LLMs increasingly require surgical model editing to enhance domain-specific capabilities without incurring the computational cost or catastrophic forgetting associated with full fi…