Distilling hidden representations from a curvature-regularized linearized teacher into a conventionally fine-tuned non-linear student transfers disentangled task-vector behavior, enabling effective model merging and unlearning with no inference overhead.
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Distilling Linearized Behavior into Non-Linear Fine-Tuning for Effective Task Arithmetic
Distilling hidden representations from a curvature-regularized linearized teacher into a conventionally fine-tuned non-linear student transfers disentangled task-vector behavior, enabling effective model merging and unlearning with no inference overhead.