Forward-Free LLM Depth Pruning via Weight Redundancy
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 result...
| Authors | Vincent-Daniel Yun, Woosang Lim |
|---|---|
| Primary category | cs.LG |
| Published | 9 September 2026 |
| Last updated at source | 14 September 2026 |
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