New arXiv paper proposes forward-free depth pruning for LLMs via weight redundancy

A newly posted arXiv paper introduces a technique for shrinking large language models by removing entire Transformer blocks without running any forward passes. Rather than gathering hidden states from calibration data, the method scores blocks using redundancy between their weights to decide which layers can be safely dropped. This avoids the extra compute that activation-based pruning approaches typically require.