SALAAD: A Plug-and-Play Framework for Sparse and Low-Rank Adaptation of Large Language Models
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[Submitted on 1 Feb 2026 (v1), last revised 28 May 2026 (this version, v3)]
Reliable enough to start your morning with. Toast it again tomorrow.
Summary
SALAAD is a plug-and-play framework for large language models that induces sparse and low-rank structures during training to reduce memory consumption during deployment. It uses an augmented Lagrangian approach with an adaptive controller to balance training loss and structural constraints, enabling flexible control over model capacity. The method works across different model architectures without requiring modifications, and a single training run produces a continuous spectrum of model capacities for deployment across diverse memory budgets.
Key quotes
· 4 pulledWe propose SALAAD, a plug-and-play framework applicable to different model architectures that induces sparse and low-rank structures during training.
By formulating structured weight learning under an augmented Lagrangian framework and introducing an adaptive controller that dynamically balances the training loss and structural constraints, SALAAD preserves the stability of standard training dynamics.
Experiments across model scales show that SALAAD substantially reduces memory consumption during deployment while achieving performance comparable to ad-hoc methods.
Moreover, a single training run yields a continuous spectrum of model capacities, enabling smooth and elastic deployment across diverse memory budgets without the need for retraining.
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