# Reinforced Lifelong Editing for Language Models

arXiv: https://arxiv.org/abs/2502.05759 · HTML: https://mlsanity.com/paper/2502.05759/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 46 references that are in the map.
- First posted on arXiv 9 Feb 2025.

## Builds on (most cited first, 30 of 36)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [Knowledge Neurons in Pretrained Transformers](https://mlsanity.com/paper/2104.08696/) (2021)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [Fast Model Editing at Scale](https://mlsanity.com/paper/2110.11309/) (2021)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Memory-Based Model Editing at Scale](https://mlsanity.com/paper/2206.06520/) (2022)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [Can We Edit Factual Knowledge by In-Context Learning?](https://mlsanity.com/paper/2305.12740/) (2023)
- [Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors](https://mlsanity.com/paper/2211.11031/) (2022)
- [AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models](https://mlsanity.com/paper/2410.02355/) (2024)
- [Mind the Gap: Assessing Temporal Generalization in Neural Language Models](https://mlsanity.com/paper/2102.01951/) (2021)
- [Modifying Memories in Transformer Models](https://mlsanity.com/paper/2012.00363/) (2020)
- [Transformer-Patcher: One Mistake worth One Neuron](https://mlsanity.com/paper/2301.09785/) (2023)
- [PMET: Precise Model Editing in a Transformer](https://mlsanity.com/paper/2308.08742/) (2023)
- [Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue](https://mlsanity.com/paper/2401.04700/) (2024)
- [A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models](https://mlsanity.com/paper/2501.13958/) (2025)
- [FlipAttack: Jailbreak LLMs via Flipping](https://mlsanity.com/paper/2410.02832/) (2024)
- [AnyEdit: Edit Any Knowledge Encoded in Language Models](https://mlsanity.com/paper/2502.05628/) (2025)
- [On the Role of Attention Heads in Large Language Model Safety](https://mlsanity.com/paper/2410.13708/) (2024)
- [Massive Editing for Large Language Models via Meta Learning](https://mlsanity.com/paper/2311.04661/) (2023)
- [The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse](https://mlsanity.com/paper/2402.09656/) (2024)
- [Perturbation-Restrained Sequential Model Editing](https://mlsanity.com/paper/2405.16821/) (2024)
- [Can Knowledge Editing Really Correct Hallucinations?](https://mlsanity.com/paper/2410.16251/) (2024)

## Built on by (most cited first, 30 of 34)

- [From System 1 to System 2: A Survey of Reasoning Large Language Models](https://mlsanity.com/paper/2502.17419/) (2025)
- [A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment](https://mlsanity.com/paper/2504.15585/) (2025)
- [Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models](https://mlsanity.com/paper/2503.15888/) (2025)
- [UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models](https://mlsanity.com/paper/2505.14679/) (2025)
- [Fine-tuning Done Right in Model Editing](https://mlsanity.com/paper/2509.22072/) (2025)
- [Is Model Editing Built on Sand? Revealing Its Illusory Success and Fragile Foundation](https://mlsanity.com/paper/2510.00625/) (2025)
- [ACE: Concept Editing in Diffusion Models without Performance Degradation](https://mlsanity.com/paper/2503.08116/) (2025)
- [Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization](https://mlsanity.com/paper/2507.01281/) (2025)
- [CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing](https://mlsanity.com/paper/2602.15823/) (2026)
- [POME: Post Optimization Model Edit via Muon-style Projection](https://mlsanity.com/paper/2510.06627/) (2025)
- [Are We Evaluating the Edit Locality of LLM Model Editing Properly?](https://mlsanity.com/paper/2601.17343/) (2026)
- [PropMEND: Hypernetworks for Knowledge Propagation in LLMs](https://mlsanity.com/paper/2506.08920/) (2025)
- [PromptCD: Test-Time Behavior Enhancement via Polarity-Prompt Contrastive Decoding](https://mlsanity.com/paper/2602.20696/) (2026)
- [From Construction to Injection: Edit-Based Fingerprints for Large Language Models](https://mlsanity.com/paper/2509.03122/) (2025)
- [Energy-Regularized Sequential Model Editing on Hyperspheres](https://mlsanity.com/paper/2510.01172/) (2025)
- [Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation](https://mlsanity.com/paper/2511.05852/) (2025)
- [From Backward Spreading to Forward Replay: Revisiting Target Construction in LLM Parameter Editing](https://mlsanity.com/paper/2605.00358/) (2026)
- [DualEdit: Mitigating Safety Fallback in LLM Backdoor Editing via Affirmation-Refusal Regulation](https://mlsanity.com/paper/2506.13285/) (2025)
- [EMSEdit: Efficient Multi-Step Meta-Learning-based Model Editing](https://mlsanity.com/paper/2508.04012/) (2025)
- [EtCon: Edit-then-Consolidate for Reliable Knowledge Editing](https://mlsanity.com/paper/2512.04753/) (2025)
- [Norm Anchors Make Model Edits Last](https://mlsanity.com/paper/2602.02543/) (2026)
- [The Model Agreed, But Didn't Learn: Diagnosing Surface Compliance in Large Language Models](https://mlsanity.com/paper/2604.05995/) (2026)
- [HiEdit: Lifelong Model Editing with Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2604.11214/) (2026)
- [BetaEdit: Null-Space Constrained Sequential Model Editing](https://mlsanity.com/paper/2605.09285/) (2026)
- [Modality-Decoupled Online Recursive Editing](https://mlsanity.com/paper/2605.20273/) (2026)
- [Epistemic Goggles: A Pretrained Module that Induces an Epistemic Frame via Gradient Editing](https://mlsanity.com/paper/2607.01690/) (2026)
- [Targeted Recovery of Weight-Space Mechanisms From Neural Networks](https://mlsanity.com/paper/2607.13047/) (2026)
- [ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models](https://mlsanity.com/paper/2607.26455/) (2026)
- [Towards Evolving Context Parameterization for Large Language Models](https://mlsanity.com/paper/2609.14168/) (2026)
- [Causal Path Alignment: Anchoring the Optimization Trajectory for Controllable In-Parameter Knowledge Editing](https://mlsanity.com/paper/2506.04042/) (2025)

## Abstract (arXiv)

Large language models (LLMs) acquire information from pre-training corpora, but their stored knowledge can become inaccurate or outdated over time. Model editing addresses this challenge by modifying model parameters without retraining, and prevalent approaches leverage hypernetworks to generate these parameter updates. However, they face significant challenges in lifelong editing due to their incompatibility with LLM parameters that dynamically change during the editing process. To address this, we observed that hypernetwork-based lifelong editing aligns with reinforcement learning modeling and proposed RLEdit, an RL-based editing method. By treating editing losses as rewards and optimizing hypernetwork parameters at the full knowledge sequence level, we enable it to precisely capture LLM changes and generate appropriate parameter updates. Our extensive empirical evaluation across several LLMs demonstrates that RLEdit outperforms existing methods in lifelong editing with superior effectiveness and efficiency, achieving a 59.24% improvement while requiring only 2.11% of the time compared to most approaches. Our code is available at: https://github.com/zhrli324/RLEdit.