In this work, we employ weight-based and subspace-based merging methods to create universal models for the MoCG task, facilitating alignment fine-tuning in a training setting with minimal data. However, both are static merging methods. This means that the merged model remain the same for all samples or tasks. Given that there are differences between input samples/tasks, the models' ability may vary when processing different samples/tasks. In the future, we aim to investigate dynamically merging models (or subsets of layers) based on the samples/tasks during the inference phase .