DualOptim+: Bridging Shared and Decoupled Optimizer States for Better Machine Unlearning in Large Language Models

2026-05-20

AI Summary

This work addresses the challenge in large language models of simultaneously unlearning harmful information while preserving useful knowledge. To this end, the authors propose DualOptim, a framework that models a shared base state for general representations and employs an incremental state to capture task-specific residual updates for forgetting and retention. A gradient conflict-aware dual-state optimization mechanism dynamically balances these two objectives by aligning their update directions. Furthermore, the method is enhanced with low-bit quantization to yield DualOptim+ 8bit, a memory-efficient variant operating at 8-bit precision. Experimental results demonstrate that DualOptim consistently outperforms existing approaches across diverse scenarios—including synthetic and real-world unlearning, safety alignment, and multi-task learning—achieving a superior trade-off between effective forgetting and knowledge retention.

Abstract

We propose DualOptim+, a novel optimization framework for improving machine unlearning in large language models. It introduces a base state to capture common representations shared by forgetting and retaining objectives and delta states to preserve objective-specific residuals. This architecture allows the optimizer to adaptively bridge shared and decoupled states based on the directional conflict between forgetting and retaining gradients. We further introduce DualOptim+ 8bit, a quantized variant that reduces memory overhead without compromising performance. Extensive experiments across fictitious and real-world unlearning, safety alignment, and multi-task learning tasks demonstrate that DualOptim+ consistently achieves a superior trade-off between different objectives. Codes are available at https://github.com/CityU-MLO/DualOptimPlus.

Problem

• machine unlearning
• large language models
• optimizer states
• forgetting
• retaining

Innovation

• machine unlearning
• optimizer state
• shared and decoupled representation
• gradient conflict
• quantized optimization