Do We Really Need Hypernetworks for Sequential Meta Model Editing?

Meta-learning-based model editors commonly employ a learned transformation, often implemented by a hypernetwork, to transform the first-step edit gradient before constructing the final parameter update. We ask how much these learned transformations actually contribute to sequential meta model editing. Across multiple meta editors, language models, and datasets, we find limited additional benefit from the learned transformation: untransformed updates remain highly competitive and can even outperform their transformed counterparts. Further analysis shows that much of the editing capability is already supported by the underlying update formulation. Motivated by these findings, we propose RawEdit, a hypernetwork-free meta editor that directly uses the raw first-step gradient and strengthens the update formulation through a direct residual and history-aware regularization. RawEdit learns only two scalars per edited layer to control edit strength and regularization while explicitly accounting for previously edited directions. Extensive experiments show that RawEdit consistently achieves strong editing performance across models and datasets, outperforming existing editors by up to 20% in efficacy, generality, and locality, while substantially reducing computational cost and remaining stable under long-term sequential editing with up to $20,000$ edits. Moreover, compared with subject-based editors, RawEdit effectively mitigates superficial editing and remains applicable to challenging settings where reliance on subject representations imposes inherent limitations. Code is available at https://anonymous.4open.science/r/RawEdit-760D.