Behaviorally Effective LoRA Writes Are Sparse and Structured
Low-rank adaptation fixes the rank of the update, but it does not identify which parts of a trained write actually carry behavior. We study that question directly and show that behaviorally effective LoRA writes are sparse, structured, and far more concentrated than the raw low-rank parameterization suggests. We use Learned-Basis LoRA, a learned-basis continuation recipe, to expose that structure. The recipe warms up an unconstrained adapter, converts its learned write columns into a module-wise orthonormal basis, freezes that basis, and continues training inside the constrained parameterization. Across 14 exact switches from unconstrained to constrained form, held-out accuracy is unchang...
| Authors | Haruto Sato, Yuki Tanaka, Ren Nakamura et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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