DualOptim+: Bridging Shared and Decoupled Optimizer States for Better Machine Unlearning in Large Language Models
arXiv:2605.21539v1 Announce Type: new 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
The increasing complexity and size of Large Language Models (LLMs) necessitate more efficient and adaptable machine unlearning techniques to meet ethical, regulatory, and practical demands.
Efficient machine unlearning in LLMs is crucial for data privacy, model compliance, and the ability to update models without complete retraining, especially as AI adoption grows across sensitive applications.
Optimized unlearning methods like DualOptim+ could make LLMs more adaptable and maintainable, reducing the computational and financial burden of removing specific data while preserving overall model utility.
- · LLM developers
- · Cloud AI providers
- · Ethical AI advocates
- · Data privacy-focused industries
- · Inefficient unlearning methods
- · Organizations with high retraining costs
Improved machine unlearning techniques will lead to more robust and compliant large language models.
The reduced cost and complexity of unlearning will accelerate the adoption of LLMs in highly regulated sectors.
Easier data removal could inadvertently lead to less stringent data governance practices if unlearning is seen as a universal fix.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG