SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling

• URL: http://arxiv.org/abs/2506.04179v1
• Date: Wed, 04 Jun 2025 17:26:31 GMT
• Title: SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling
• Authors: Anhao Zhao, Fanghua Ye, Yingqi Fan, Junlong Tong, Zhiwei Fei, Hui Su, Xiaoyu Shen,
• Abstract summary: We introduce SkipGPT, a dynamic layer pruning framework to optimize large language models.<n>We show that SkipGPT reduces over 40% of model parameters while matching or exceeding the performance of the original dense model.
• Score: 16.742839354514512
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) achieve remarkable performance across tasks but incur substantial computational costs due to their deep, multi-layered architectures. Layer pruning has emerged as a strategy to alleviate these inefficiencies, but conventional static pruning methods overlook two critical dynamics inherent to LLM inference: (1) horizontal dynamics, where token-level heterogeneity demands context-aware pruning decisions, and (2) vertical dynamics, where the distinct functional roles of MLP and self-attention layers necessitate component-specific pruning policies. We introduce SkipGPT, a dynamic layer pruning framework designed to optimize computational resource allocation through two core innovations: (1) global token-aware routing to prioritize critical tokens, and (2) decoupled pruning policies for MLP and self-attention components. To mitigate training instability, we propose a two-stage optimization paradigm: first, a disentangled training phase that learns routing strategies via soft parameterization to avoid premature pruning decisions, followed by parameter-efficient LoRA fine-tuning to restore performance impacted by layer removal. Extensive experiments demonstrate that SkipGPT reduces over 40% of model parameters while matching or exceeding the performance of the original dense model across benchmarks. By harmonizing dynamic efficiency with preserved expressivity, SkipGPT advances the practical deployment of scalable, resource-aware LLMs. Our code is publicly available at: https://github.com/EIT-NLP/SkipGPT.

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