Pilot: Transferring LoRA Adapters with One-Time Meta-Training

LoRA adapters trained on one base model may not retain their task performance when transferred to an updated version. Transferring them requires recovering their task behavior while preserving the target model's instruction-following ability. We propose , a transfer operator trained once for a source and target model pair and reused across adapters. first constructs a closed-form approximation of an adapter's effect on the target from unlabeled activations, then applies learned low-rank corrections. Matching intermediate module outputs alone does not ensure correct or complete answers, so the corrections receive feedback through the frozen target model. During supervised meta-training, these corrections are optimized for accurate, complete task responses, with a KL penalty that limits changes to the target model's behavior on general instructions. Once trained, the operator transfers held-out adapters from the same task family between models with matching architectures without further labels or gradient computation. For different architectures, -X constructs a transfer basis from source-generated answers using a target backward pass, avoiding iterative fine-tuning for each adapter. Experiments within and across model families show that transferred adapters achieve task accuracy competitive with supervised target adaptation while largely retaining the target model's instruction-following ability.