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Life sciences · Preprint
arXiv · September 9, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint introduces Weight-Redundancy Pruning (WRP), a forward-free method for reducing large language model inference cost by removing redundant Transformer blocks using weight similarity without calibration data. The method is benchmarked against existing approaches across multiple settings and reportedly outperforms forward-free magnitude pruning while approaching activation-based performance, but the work has not undergone peer review and no statistical significance testing or confidence intervals are reported.
Preprint. Intervention: Weight-Redundancy Pruning (WRP) method comparing attention output and MLP down-projection weights to estimate inter-layer redundancy and select blocks for removal.. Compared with: Existing forward-free magnitude pruning and activation-based depth-pruning methods.
WRP consistently outperforms existing forward-free magnitude pruning across multiple pruning settings WRP approaches the performance of activation-based methods without requiring forward passes or calibration data Method estimates inter-layer redundancy by comparing attention output and MLP down-projection weights across layers
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This is an unrefereed arXiv preprint describing a novel computational method for model compression; it presents empirical comparisons but lacks peer review and clinical/medical applicability.
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Depth pruning reduces large language model (LLM) inference cost by removing complete Transformer blocks. Activation-based methods collect hidden states through forward passes on calibration data, while existing forward-free methods score each Transformer block separately without measuring similarity between blocks. We propose Weight-Redundancy Pruning (WRP), a forward-free depth-pruning method that estimates inter-layer redundancy from checkpoint weights to select blocks without calibration data or model forward passes. WRP compares attention output and MLP down-projection weights across layers and combines their pairwise similarities with relative projection-scale information. The resulting all-pairs similarity matrix guides layer grouping and block selection. Across multiple pruning settings, model families, and downstream tasks, WRP consistently outperforms existing forward-free magnitude pruning and approaches the performance of activation-based methods.
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