Activation-Guided Consensus Merging for Large Language Models

Recent research has increasingly focused on reconciling the reasoning capabilities of System 2 with the efficiency of System 1. While existing training-based and prompt-based approaches face significant challenges in terms of efficiency and stability, model merging emerges as a promising strategy to integrate the diverse capabilities of different Large Language Models (LLMs) into a unified model. However, conventional model merging methods often assume uniform importance across layers, overlooking the functional heterogeneity inherent in neural components. To address this limitation, we propose \textbf{A}ctivation-Guided \textbf{C}onsensus \textbf{M}erging (\textbf{ACM}), a plug-and-play merging framework that determines layer-specific merging coefficients based on mutual information between activations of pre-trained and fine-tuned models. ACM effectively preserves task-specific capabilities without requiring gradient computations or additional training. Extensive experiments on Long-to-Short (L2S) and general merging tasks demonstrate that ACM consistently outperforms all baseline methods. For instance, in the case of Qwen-7B models, TIES-Merging equipped with ACM achieves a \textbf{55. 3\%} reduction in response length while simultaneously improving reasoning accuracy by \textbf{1. 3} points. We submit the code with the paper for reproducibility, and it will be publicly available.

Authors

• Yuxuan Yao
• Shuqi LIU
• Zehua Liu School of Computer Science and Engineering, Beihang University, Beijing, China
• Qintong Li
• Mingyang Liu
• Xiongwei Han Huawei Technologies
• Zhijiang Guo
• Han Wu
• Linqi Song

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