Merged Model

Model merging combines multiple specialized machine learning models into a single, more robust and efficient model, aiming to improve generalization, reduce resource consumption, and facilitate decentralized model development. Current research focuses on scaling merging techniques to larger models (e.g., those with billions of parameters), exploring various merging algorithms (including averaging, task arithmetic, and permutation-based methods), and addressing challenges like task interference and representation bias across different model architectures (e.g., CNNs, Vision Transformers, and Language Models). This field is significant because it offers a pathway to leverage the strengths of numerous pre-trained models without requiring access to their original training data, leading to improved performance and resource efficiency in various applications.