Reinforced Lifelong Editing for Language Models

2025-02-09

AI Summary

Large language models (LLMs) suffer from dynamic knowledge obsolescence over time, and existing parameter-editing methods lack support for lifelong, continuous model updating. Method: This paper introduces the first reinforcement learning (RL)-based framework for lifelong model editing, where edit loss serves as the reward signal; a lightweight hypernetwork is optimized over full knowledge sequences to precisely model and adaptively correct LLM parameter evolution—without full retraining and while accommodating dynamic parameter changes. Contribution/Results: The approach enables efficient, scalable, and incremental knowledge updates. Experiments across multiple mainstream LLMs demonstrate a 59.24% improvement in editing accuracy and a 97.89% reduction in inference latency (i.e., only 2.11% of baseline runtime), significantly outperforming state-of-the-art lifelong editing techniques.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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models (LLMs) acquire information from pre-training corpora, but their stored knowledge can become inaccurate or outdated over time.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.

Problem

• Lifelong editing for outdated knowledge
• Dynamic parameter changes in LLMs
• Efficient and effective model updates

Innovation

• Reinforcement learning for model editing
• Hypernetwork optimization in LLMs
• Lifelong editing with minimal retraining