First-Order Predictable but Pairwise Fragile: Local Task Adaptation in Trained Transformers
Task arithmetic, sequential fine-tuning, activation steering, and first-order random search all operate through relatively small perturbations around an already trained checkpoint, and they rely on different local approximations: individual perturbations should be first-order predictable, task updates should compose with controlled interference, useful tangent structure should be stable and possible to estimate, and weight edits should have counterparts in representation space. We measure 8 such properties with the same harness around a multitask LoRA operating point, on 9 transformers (82M-7B), with a prospectively registered property list, thresholds, and test split. We find a shared on...
| Authors | Irina Piontkovskaia, Sergey Nikolenko |
|---|---|
| Primary category | cs.LG |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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