CT-Merging: Consensus Directions and Task-Specific Scaling for LoRA Adapter Merging
LoRA merging methods increasingly operate on the low-rank structure of task updates, yet how the common subspace is estimated and how coefficients are assigned after recomposition are rarely compared directly. We propose CT-Merging, which estimates common directions from averaged task subspace projectors and assigns a separate residual scale to each task. Projector averaging selects directions supported across task subspaces without weighting them by singular magnitude, while task-specific scaling removes component-wise magnitude variation and preserves scale differences across tasks. On the released KnOTS CLIP adapters, CT-Merging achieves the best average and worst-task normalized accur...
| Authors | Keumseo Ryum, Joonhyuk Kang |
|---|---|
| Primary category | cs.LG |
| Published | 18 July 2026 |
| Last updated at source | 25 September 2026 |
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