Knowledge Editing (KE) aims to update specific facts in large language models (LLMs) without full retraining. Prior efforts sought to tune the knowledge layers of LLMs, achieving near-perfect performance in teacher-forced evaluations. However, under realistic autoregressive generation evaluation, their effectiveness remains limited by two core challenges: (1) even when methods successfully inject new knowledge, pre-trained capabilities can degrade, and (2) more critically, the injected parametric knowledge is not reliably expressed at inference time. To this end, we formalize knowledge editing as a two-stage problem and propose EtCon, an edit-then-consolidate paradigm that couples targeted edits with post-edit consolidation. Within EtCon, the edit stage applies Targeted Proximal Supervised Fine-Tuning (TPSFT) to update parametric knowledge with minimal disruption to pre-trained capabilities. The consolidation stage applies Group Relative Policy Optimization (GRPO) to align the model’s autoregressive generation with the edited parametric knowledge. Extensive experiments demonstrate that our instantiation (TPSFT + GRPO) improves editing reliability and real-world generalization while preserving pre-trained capabilities, and that EtCon consistently improves existing editing methods when instantiated with them.