@inproceedings{xing-etal-2026-efficientllm,
    title = "{E}fficient{LLM}: Unified Pruning-Aware Pretraining for Auto-Designed Compact Language Models",
    author = "Xing, Xingrun  and
      Liu, Zheng  and
      Xiao, Shitao  and
      Gao, Boyan  and
      Liang, Yiming  and
      Lin, Haokun  and
      Zeng, Xianlin  and
      Li, Guoqi  and
      Zhang, Jiajun",
    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.355/",
    pages = "7813--7830",
    ISBN = "979-8-89176-390-6",
    abstract = "Modern large language models (LLMs) driven by scaling laws achieve emergent intelligence in large model sizes. Recently, the increasing concerns about cloud costs, latency and privacy make it an urgent requirement to develop compact edge language models. Distinguished from direct pretraining that bounded by parameter scaling law, this work proposes the unified pruning-aware pretraining, focusing on pretraining compact models while preserving performance of much larger source models, termed EfficientLLM. It features following characteristics: 1) Pruning in Pretraining Corpus: we introduce minimal parameter groups to decouple LLMs and continuously optimize model architecture with classic pruning methods like LLM-Pruner and SparseGPT during pretraining. We reveal that it achieves top-quality compact language models to scale up LLM pruning to large scale pretraining. 2) Auto-Designed Architecture: the LLM architecture is auto-designed during saliency-driven pruning, unifying pretraining, architectural design, and parameter pruning into a single process. Based on these, EfficientLLM significantly outperforms directly pretrained baselines with $100M \sim 1B$ parameters, such as MobileLLM, SmolLM, Qwen2.5-0.5B, OLMo-1B, Llama3.2-1B in commen sense benchmarks, which bridges the performance gap between traditional LLM compression and direct pretraining. We open source on https://github.com/Xingrun-Xing2/EfficientLLM."
}