Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging
Authors: name: Han Wu; name: Yuxuan Yao; name: Shuqi Liu; name: Zehua Liu; name: Xiaojin Fu; name: Xiongwei Han; name: Xing Li; name: Hui-Ling Zhen; name: Tao Zhong; name: Mingxuan Yuan
Published: 2025-03-26 15:34:37

Abstract: The transition from System 1 to System 2 reasoning in large language models
(LLMs) has marked significant advancements in handling complex tasks through
deliberate, iterative thinking. However, this progress often comes at the cost
of efficiency, as models tend to overthink, generating redundant reasoning
steps without proportional improvements in output quality. Long-to-Short (L2S)
reasoning has emerged as a promising solution to this challenge, aiming to
balance reasoning depth with practical efficiency. While existing approaches,
such as supervised fine-tuning (SFT), reinforcement learning (RL), and prompt
engineering, have shown potential, they are either computationally expensive or
unstable. Model merging, on the other hand, offers a cost-effective and robust
alternative by integrating the quick-thinking capabilities of System 1 models
with the methodical reasoning of System 2 models. In this work, we present a
comprehensive empirical study on model merging for L2S reasoning, exploring
diverse methodologies, including task-vector-based, SVD-based, and
activation-informed merging. Our experiments reveal that model merging can
reduce average response length by up to 55% while preserving or even improving
baseline performance. We also identify a strong correlation between model scale
and merging efficacy with extensive evaluations on 1.5B/7B/14B/32B models.
Furthermore, we investigate the merged model's ability to self-critique and
self-correct, as well as its adaptive response length based on task complexity.
Our findings highlight model merging as a highly efficient and effective
paradigm for L2S reasoning, offering a practical solution to the overthinking
problem while maintaining the robustness of System 2 reasoning. This work can
be found on Github https://github.com/hahahawu/Long-to-Short-via-Model-Merging.