Revisiting Model Interpolation for Efficient Reasoning
Authors: name: Taiqiang Wu, name: Runming Yang, name: Tao Liu, name: Jiahao Wang, name: Ngai Wong
Organization: The University of Hong Kong

Model merging, typically on Instruct and Thinking models, has shown
remarkable performance for efficient reasoning. In this paper, we
systematically revisit the simplest merging method that interpolates two
weights directly. Particularly, we observe that model interpolation follows a
three-stage evolutionary paradigm with distinct behaviors on the reasoning
trajectory. These dynamics provide a principled guide for navigating the
performance-cost trade-off. Empirical results demonstrate that a strategically
interpolated model surprisingly surpasses sophisticated model merging baselines
on both efficiency and effectiveness. We further validate our findings with
extensive ablation studies on model layers, modules, and decoding strategies.
Ultimately, this work demystifies model interpolation and offers a practical
framework for crafting models with precisely targeted reasoning capabilities.
Code is available at https://github.com/wutaiqiang/MI{Github}.