@inproceedings{liu-etal-2026-laco,
    title = "{L}a{C}o: Layer-wise Compensation for Pruned Large Language Models",
    author = "Liu, Yingen  and
      Wu, Fan  and
      Panxuyan  and
      Li, Ruihui  and
      Tang, Zhuo  and
      Li, Kenli",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1342/",
    pages = "29099--29113",
    ISBN = "979-8-89176-390-6",
    abstract = "Pruning is essential for the efficient deployment of Large Language Models (LLMs); however, it causes severe performance degradation due to the structural distortion induced by sparsity.Existing recovery strategies, such as LoRA, predominantly employ global fine-tuning, often overlooking the mechanistic root of this degradation: the layer-wise accumulation and amplification of local errors. To address this limitation, we propose LaCo(Layer-wise Compensation), a framework that reorients the recovery paradigm from global adaptation to hierarchical representation alignment.By sequentially optimizing each layer to reconstruct the model{'}s hidden states, LaCo effectively intercept the error propagation chain at its source.Extensive experiments demonstrate that LaCo surpasses parameter-efficient baselines in both perplexity reduction and zero-shot reasoning.Notably, it reduces recovery-time memory usage to approximately $1/7$ of the baseline and requires only 2,048 unlabeled samples to match a LoRA model trained on 50k examples{---}achieving a $\sim25\times$ improvement in data efficiency."
}