Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM

Abstract:Large language models (LLM) and vision-language models (VLM) have achieved state-of-the-art performance, but they impose significant memory and computing challenges in deployment. We present a novel low-rank compression framework to address this challenge. First, we upper bound the change of network loss via layer-wise activation-based compression errors, filling a theoretical gap in the literature. We then formulate low-rank model compression as a bi-objective optimization and prove that a single uniform tolerance yields surrogate Pareto-optimal heterogeneous ranks. Based on our theoretical insights, we propose Pareto-Guided Singular Value Decomposition (PGSVD), a zero-shot pipeline that improves activation-aware compression via Pareto-guided rank selection and alternating least-squares implementation. We apply PGSVD to both LLM and VLM, showing better accuracy at the same compression levels and inference speedup.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2510.05544 [cs.CL]

(or arXiv:2510.05544v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2510.05544 Focus to learn more arXiv-issued DOI via DataCite

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