On the Editability of Delta Parameters in Post-Trained Models

摘要

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.