When Edits Accumulate: Constraint-Aware Policy Learning for Lifelong Model Editing

Lifelong model editing aims to continuously update knowledge in large language models without full retraining. However, sequential parameter updates can accumulate interference and progressively damage previously edited or unrelated knowledge. To address this challenge, we propose SeqConEdit, a constraint-aware policy learning framework that formulates lifelong editing as a constrained sequential decision problem. Conditioned on the current editing request and accumulated editing history, SeqConEdit adaptively coordinates knowledge incorporation and preservation through a state-conditioned policy. Specifically, we introduce a Preservation-Key Constraint (PKC) to suppress interference along protected knowledge directions and a Preservation Trace Buffer (PTB) to retain historical preservation information across editing steps. Building on these mechanisms, constraint-aware policy optimization dynamically balances editing and preservation according to feedback from the sequential editing process. Extensive experiments show that SeqConEdit consistently improves lifelong editing performance, outperforming HiEdit by up to 11.94% in average performance while substantially enhancing long-term retention of edited knowledge. Further analyses demonstrate improved knowledge preservation, cross-dataset generalization, and policy optimization stability under long edit streams. The source code and implementation details are publicly available at https://anonymous.4open.science/r/SeqConEdit.