Locality-Aware Redundancy Pruning for LLM Depth Compression
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Computer Science > Machine Learning
Title:Locality-Aware Redundancy Pruning for LLM Depth Compression
Abstract:Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving inference efficiency. Existing one-shot pruning methods rely on local layer importance or fixed redundancy assumptions across architectures. We propose Locality-Aware Redundancy Pruning (LoRP), a training-free one-shot depth pruning framework guided by representation locality. We show that inter-layer redundancy can be either localized or globally distributed depending on the LLM architecture. To characterize this phenomenon, we introduce Representation Locality Score (RLS), derived from global inter-layer hidden-state similarity. Using a small calibration set, LoRP computes pairwise layer similarity, clusters layers by representational similarity, and allocates pruning according to residual intra-cluster redundancy. Experiments across diverse LLM families show improvements in both perplexity and downstream task accuracy.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.27786 [cs.LG] |
| (or arXiv:2605.27786v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.27786
arXiv-issued DOI via DataCite (pending registration)
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