Title: Aging with GRACE: Lifelong Model Editing with Key-Value Adaptors. Abstract: Large language models often err during deployment, either due to non-representative training data or distribution shift in the test set. Recently, model editors have been proposed to fix errors by adjusting a pre-trained model's weights. So far, however, existing model editors fail when making sequential edits by quickly decaying a model's performance on its upstream data. Further, when editing deployed online models, they quickly forget how to fix previously-seen mistakes. We advance beyond these existing methods by proposing and studying a novel Lifelong Model Editing setting, where errors stream into a deployed model and we update the model to correct its predictions without influencing its predictions for unrelated inputs. Towards effective methods in this challenging setting, we propose with General Retrieval Adaptors for Continual Editing, or GRACE. GRACE is a new Key-Value framework that casts model editing as a codebook update problem. The proposed approach edits selected model layers by caching activations that are queried using embeddings from the previous layer. The cached activations are trained to correct a model's predictions, treating future layers as a decoder. As edits stream in, the keys and values of a GRACE layer are updated while the model weights remain frozen, ensuring similar edits are treated similarly without altering the model's performance on unrelated instances. Experimentally, we show that \method substantially improves over recent model editors.