Knowledge editing updates facts in large language models while preserving unrelated knowledge. Most prior work studies object editing, which modifies the object under a fixed subject–relation pair. We instead study relation replacement editing, where an obsolete relation must be suppressed as a new one is introduced. We systematically study this setting with a dedicated benchmark and method. We introduce ReEditBench to evaluate replacement rather than association addition. Existing editors exhibit two major failures: obsolete-relation retention remains as high as 98.20% after successful edits, and difficult samples often destabilize editing. To address this, we propose R²Edit, a two-stage framework for relation replacement. Its target-smoothed Forget stage reduces obsolete-relation activation without fixed/random pseudo-target bias, while its curriculum-guided Edit stage progressively injects new relations from easy to hard samples. Across multiple LLMs, R²Edit improves replacement success, reduces retention, and preserves generalization. Stress tests on difficult subsets further demonstrate the robustness of the Edit stage. Code is available at: https://github.com/caoxu0109/R2Edit.