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cs.LG2026
Rethinking Layer Redundancy: Calibration Matters More Than Search in LLM Depth Pruning
Minkyu Kim, Vincent-Daniel Yun, Youngrae Kim +3
Depth pruning improves the inference efficiency of large language models by removing Transformer blocks. Prior work typically treats layer redundancy as an inherent structural prop…
cs.LG2026
Locality-Aware Redundancy Pruning for LLM Depth Compression
Vincent-Daniel Yun, Youngrae Kim, Woosang Lim +3
Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving inference efficiency. Existing…