Synergy Oriented LeARning

Synergy-oriented learning (SOLAR) focuses on improving machine learning efficiency and performance by leveraging the combined strengths of multiple models or learning approaches. Current research explores SOLAR in diverse applications, including semantic segmentation, solving partial differential equations, and enhancing large language models (LLMs) through techniques like multi-task learning and iterative refinement between LLMs and smaller, specialized models. This approach offers significant potential for reducing training costs, improving generalization, and enabling new capabilities in various fields, from medical diagnosis to robotics and scientific computing.