arXiv:2605.29075v1 Announce Type: new Abstract: LLMs encode both general capabilities and domain-specific knowledge in a single set of parameters. We ask whether this capacity can be reorganized: keeping broadly useful computation in a shared backbone, while moving specialized knowledge into external memory modules. We propose \emph{knowledge offloading} (KOFF), a framework for decomposing a pretrained LLM into a sparse shared backbone and domain-specific memories. Starting from a frozen base model, we jointly learn a structured pruning mask and lightweight recovery modules, implemented as LoR
The increasing scale and computational demands of large language models necessitate innovation in efficiency and modularity to sustain progress and broaden deployment.
This development offers a pathway to more efficient, adaptable, and potentially cheaper LLMs, which is critical for their widespread commercial and research adoption.
LLMs can now be systematically decomposed, allowing for a shared backbone across multiple knowledge domains and specialized, more manageable memory modules, potentially reducing redundant compute and enabling more agile updates.
- · AI compute infrastructure providers
- · LLM developers and researchers
- · Companies deploying custom LLMs
- · Edge AI applications
- · monolithic LLM architectures
- · organizations with limited compute budgets relying on large, undifferentiated mo
Reduced inference costs and improved fine-tuning efficiency for LLMs.
Accelerated development of domain-specific AI applications due to easier knowledge integration and updates.
Democratization of advanced AI capabilities as smaller, more efficient models become viable for a wider range of users and devices.
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