OpenNovelty.org

Fine-tuning Done Right in Model Editing

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
model editingfine-tuningknowledge update
Abstract:

Fine-tuning, a foundational method for adapting large language models, has long been considered ineffective for model editing. Here, we challenge this belief, arguing that the reported failure arises not from the inherent limitation of fine-tuning itself, but from adapting it to the sequential nature of the editing task, a single-pass depth-first pipeline that optimizes each sample to convergence before moving on. While intuitive, this depth-first pipeline coupled with sample-wise updating over-optimizes each edit and induces interference across edits. Our controlled experiments reveal that simply restoring fine-tuning to the standard breadth-first (i.e., epoch-based) pipeline with mini-batch optimization substantially improves its effectiveness for model editing. Moreover, fine-tuning in editing also suffers from suboptimal tuning parameter locations inherited from prior methods. Through systematic analysis of tuning locations, we derive LocFT-BF, a simple and effective localized editing method built on the restored fine-tuning framework. Extensive experiments across diverse LLMs and datasets demonstrate that LocFT-BF outperforms state-of-the-art methods by large margins. Notably, to our knowledge, it is the first to sustain 100K edits and 72B-parameter models,10 ×\times beyond prior practice, without sacrificing general capabilities. By clarifying a long-standing misconception and introducing a principled localized tuning strategy, we advance fine-tuning from an underestimated baseline to a leading method for model editing, establishing a solid foundation for future research.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper challenges the conventional view that fine-tuning is ineffective for model editing by proposing a breadth-first optimization pipeline and localized parameter selection. It resides in the 'Fine-Tuning for Editing' leaf under Core Editing Methods and Frameworks, which contains only two papers total. This sparse population suggests the research direction—adapting fine-tuning specifically for editing tasks—remains relatively unexplored compared to more crowded areas like locate-and-edit approaches or hypernetwork-based methods. The work's positioning indicates it occupies a niche where fine-tuning paradigms are being reconsidered for editing contexts.

The taxonomy reveals neighboring leaves include Locate-and-Edit Approaches (three papers), Hypernetwork-Based Editing (two papers), and Geometric and Subspace Methods (one paper), all within the same Core Editing Methods branch. These sibling categories pursue fundamentally different strategies: explicit parameter localization, meta-learned parameter shifts, or geometric analysis of update spaces. The paper's focus on restoring standard fine-tuning practices diverges from these specialized techniques, instead arguing that conventional training protocols can be effective when properly adapted. This positions the work at the intersection of classical optimization and modern editing requirements.

Among 25 candidates examined across three contributions, the analysis found limited prior work overlap. The breadth-first pipeline contribution examined five candidates with zero refutations, suggesting this specific optimization strategy is relatively novel within the search scope. The localized fine-tuning method examined ten candidates, again with no refutations, indicating the principled location selection approach appears distinct from examined alternatives. However, the scalability claim (100K edits, 72B parameters) examined ten candidates and found one refutable instance, suggesting prior work may have achieved comparable scale, though the search scope remains limited to top-K semantic matches.

The analysis reflects a constrained literature search rather than exhaustive coverage, examining 25 candidates from semantic retrieval. The sparse taxonomy leaf and low refutation rates suggest the work explores a relatively underinvestigated direction, though the single refutation on scalability claims indicates some overlap with existing capabilities. The findings should be interpreted as preliminary signals based on available search results, not definitive assessments of absolute novelty across the entire model editing literature.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
25
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: model editing in large language models. The field has evolved into a rich landscape organized around several major branches. Core Editing Methods and Frameworks encompass foundational techniques such as fine-tuning approaches and specialized editing algorithms, while Editing Scope and Modality Extensions address cross-lingual and multimodal scenarios. Lifelong and Sequential Editing tackles the challenge of applying multiple edits over time without catastrophic forgetting, and Evaluation, Analysis, and Side Effects investigates metrics and unintended consequences of modifications. Parameter-Efficient Adaptation Methods explore lightweight alternatives like LoRA[3] and related variants, Alternative Knowledge Update Paradigms consider retrieval-augmented or memory-based strategies, and Specialized Applications and Safety focus on domain-specific uses and safeguarding model behavior. Together, these branches reflect a transition from isolated editing operations to systematic frameworks that balance efficiency, generalization, and robustness. Recent work highlights contrasting philosophies: some studies pursue parameter-efficient fine-tuning to minimize computational overhead, while others emphasize full-parameter updates or hybrid strategies to preserve model capabilities. Fine-tuning Done Right[0] sits within the Core Editing Methods branch alongside fine-tuning-centric approaches, exploring how careful tuning can mitigate issues like overfitting or knowledge degradation. This contrasts with neighbors such as Forgetting Before Learning[42], which investigates whether selective forgetting can improve subsequent edits. Meanwhile, works like Knowledge Editing Survey[1] and Comprehensive Knowledge Editing[5] provide broader perspectives on trade-offs between editing precision and side effects, and Robust Model Editing[6] examines resilience under adversarial conditions. The central tension across these lines involves achieving targeted updates without harming general performance, a challenge that Fine-tuning Done Right[0] addresses by refining traditional fine-tuning protocols rather than abandoning them for more exotic paradigms.

Claimed Contributions

Restoring fine-tuning to breadth-first pipeline with mini-batch optimization

The authors demonstrate that the reported failure of fine-tuning in model editing arises from using a depth-first pipeline with sample-wise updates rather than the standard breadth-first pipeline with mini-batch gradient aggregation. Switching to the standard paradigm substantially improves editing performance.

5 retrieved papers
LocFT-BF: localized fine-tuning method with principled tuning location selection

Through systematic analysis of tuning locations across layers and modules in diverse LLMs, the authors develop LocFT-BF, which combines breadth-first pipeline, mini-batch optimization, and principled parameter location selection for effective model editing.

10 retrieved papers
First method to sustain 100K edits and 72B-parameter models

The authors demonstrate that LocFT-BF is the first model editing method capable of handling 100,000 sequential edits and scaling to 72-billion parameter models, both representing an order of magnitude beyond mainstream practice, while preserving general capabilities.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Restoring fine-tuning to breadth-first pipeline with mini-batch optimization

The authors demonstrate that the reported failure of fine-tuning in model editing arises from using a depth-first pipeline with sample-wise updates rather than the standard breadth-first pipeline with mini-batch gradient aggregation. Switching to the standard paradigm substantially improves editing performance.

Contribution

LocFT-BF: localized fine-tuning method with principled tuning location selection

Through systematic analysis of tuning locations across layers and modules in diverse LLMs, the authors develop LocFT-BF, which combines breadth-first pipeline, mini-batch optimization, and principled parameter location selection for effective model editing.

Contribution

First method to sustain 100K edits and 72B-parameter models

The authors demonstrate that LocFT-BF is the first model editing method capable of handling 100,000 sequential edits and scaling to 72-billion parameter models, both representing an order of magnitude beyond mainstream practice, while preserving general capabilities.

Table of Contents