@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T On the Editability of Delta Parameters in Post-Trained Models
%A Tang, Qiaoyu
%A Yu, Le
%A Yu, Bowen
%A Lin, Hongyu
%A Lu, Keming
%A Lu, Yaojie
%A Han, Xianpei
%A Sun, Le
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F tang-etal-2026-editability
%X 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.
%U https://aclanthology.org/2026.findings-acl.1833/
%P 36809-36824
Markdown (Informal)
[On the Editability of Delta Parameters in Post-Trained Models](https://aclanthology.org/2026.findings-acl.1833/) (Tang et al., Findings 2026)
ACL
- Qiaoyu Tang, Le Yu, Bowen Yu, Hongyu Lin, Keming Lu, Yaojie Lu, Xianpei Han, and Le Sun. 2026. On the Editability of Delta Parameters in Post-Trained Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 36809–36824, San Diego, California, United States. Association for Computational Linguistics.