Predicting Mergeability of Parameter-Efficient Fine-Tuning Updates
Low-rank adaptation (LoRA) makes it cheap to train many domain- and task-specific language model adapters, but whether two adapters can be merged is usually discovered only after both have been fully trained and evaluated. This late feedback is costly: adapters that are strong in isolation can interfere destructively once their updates are combined. We ask whether this outcome can be anticipated. We formalize adapter mergeability as the degree to which an adapter preserves its single-task utility after merging, and show that it can be forecast from signals measured in the first few percent of training -- chiefly how the low-rank updates and their gradients align across tasks and how much ...
| Authors | Lin Tang, Wei Zhang, Jing Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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