Knowledge editing aims to update factual knowledge in language models without full retraining. However, edits should also propagate to downstream queries that require combining the edited fact with other knowledge, such as multi-hop queries. Existing approaches address this propagation problem with more elaborate edit-time procedures, through augmented reasoning examples or external editing modules. In this work, we take a different approach: we meta-learn model parameters from which simple edits are more likely to propagate. We propose LEAP (Learning Edit-Adaptable Parameters), a MAML-based framework that learns edit-adaptable parameters such that simple gradient-based fine-tuning on new single-hop facts improves answers to their multi-hop consequences. Experiments on MQuAKE show that LEAP consistently improves over standard LoRA editing and achieves a favorable accuracy-efficiency trade-off compared with representative knowledge-editing baselines. Further analysis suggests that LEAP makes bridge entities more accessible and strengthens the bridge-to-answer transition needed for multi-hop reasoning. These results suggest a new direction for improving edit propagation: preparing the model itself to be more amenable to propagation-friendly updates, rather than relying only on increasingly complex edit-time procedures.