@inproceedings{tang-etal-2026-editability,
    title = "On the Editability of Delta Parameters in Post-Trained Models",
    author = "Tang, Qiaoyu  and
      Yu, Le  and
      Yu, Bowen  and
      Lin, Hongyu  and
      Lu, Keming  and
      Lu, Yaojie  and
      Han, Xianpei  and
      Sun, Le",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1833/",
    pages = "36809--36824",
    ISBN = "979-8-89176-395-1",
    abstract = "Post-training has emerged as a crucial paradigm for adapting large-scale pre-trained models to various tasks, whose effects are fully reflected by delta parameters (i.e., the disparity between post-trained and pre-trained parameters).While numerous studies have explored delta parameter properties via operations like pruning, quantization, low-rank approximation, and extrapolation, a fundamental question remains: what properties of delta parameters are essential for maintaining performance?In this work, we investigate delta parameter properties along two dimensions: magnitude and sign. Through experiments on instruct language models, reasoning language models, and vision models, we find that delta parameters exhibit considerable editability: individual values, distribution shape, relative relationships, and even signs can be substantially modified while maintaining post-trained model{'}s performance.To understand these phenomena, we propose a loss-based local surrogate analysis that examines editing effects through a second-order Taylor expansion. Our analysis introduces the concept of editing intensity, which helps explain the stability boundaries of different editing operations."
}