Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration

Traditional machine societies rely on data-driven learning, overlooking interactions and limiting knowledge acquisition from model interplay. To address these issues, we revisit the development of machine societies by drawing inspiration from the evolutionary processes of human societies. Motivated by Social Learning (SL), this paper introduces a practical paradigm of Socialized Coevolution (SC). Compared to most existing methods focused on knowledge distillation and multi-task learning, our work addresses a more challenging problem: not only enhancing the capacity to solve new downstream tasks but also improving the performance of existing tasks through inter-model interactions. Inspired by cognitive science, we propose Dynamic Information Socialized Collaboration (DISC), which achieves SC through interactions between models specialized in different downstream tasks. Specifically, we introduce the dynamic hierarchical collaboration and dynamic selective collaboration modules to enable dynamic and effective interactions among models, allowing them to acquire knowledge from these interactions. Finally, we explore potential future applications of combining SL and SC, discuss open questions, and propose directions for future research, aiming to spark interest in this emerging and exciting interdisciplinary field. Our code will be publicly available at https://github.com/yxjdarren/SC.

Current machine learning systems predominantly rely on isolated, data-driven models, neglecting the interactive dynamics among models. This limitation hampers their ability to efficiently acquire knowledge and adapt across multiple downstream tasks.Inspired by the evolutionary and social learning processes of human societies, we propose a practical paradigm termed Socialized Coevolution (SC). Our approach enables dynamic and effective interactions among specialized models through mechanisms such as dynamic hierarchical and selective collaboration, fostering knowledge exchange and joint improvement.This paradigm advances beyond traditional knowledge distillation and multi-task learning by simultaneously enhancing performance on both new and existing tasks. It opens promising directions for developing intelligent systems capable of collaborative learning, with broad implications for interdisciplinary machine learning research and practical applications.