CoSpaDi: Compressing LLMs via Calibration-Guided Sparse Dictionary Learning

📰 ArXiv cs.AI

Learn to compress LLMs using CoSpaDi, a calibration-guided sparse dictionary learning method, to reduce model size while maintaining accuracy

advanced Published 5 May 2026

Action Steps

Apply CoSpaDi to compress LLMs via sparse dictionary learning
Use calibration-guided techniques to optimize compression
Evaluate the compressed model's accuracy and compare with the original model
Fine-tune the compressed model for specific tasks if necessary
Deploy the compressed model in resource-constrained environments

Who Needs to Know This

AI engineers and researchers working with large language models can benefit from this method to improve model efficiency and deployment

Key Insight

💡 CoSpaDi provides a flexible and efficient way to compress LLMs without significant accuracy loss

Key Takeaways

Learn to compress LLMs using CoSpaDi, a calibration-guided sparse dictionary learning method, to reduce model size while maintaining accuracy

Full Article

Title: CoSpaDi: Compressing LLMs via Calibration-Guided Sparse Dictionary Learning

Abstract:
arXiv:2509.22075v5 Announce Type: replace-cross Abstract: Post-training compression of large language models (LLMs) often relies on low-rank weight approximations that represent each column of the weight matrix in a shared low-dimensional subspace. This strategy is computationally efficient but the underlying constraint can be overly rigid for heterogeneous projection weights and may incur avoidable accuracy loss. We propose CoSpaDi (Compression via Sparse Dictionary Learning), a training-free f

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