Sub Task Decomposition

Sub-task decomposition involves breaking down complex tasks into smaller, more manageable sub-tasks to improve efficiency and tractability in various domains. Current research focuses on automating this decomposition process using foundation models like large language models and vision-language models, as well as developing algorithms to optimize the scheduling and execution of these sub-tasks, particularly in multi-agent and resource-constrained environments. This approach shows promise in accelerating the training of complex systems, improving resource utilization, and enabling the creation of larger, more diverse datasets for machine learning, with applications ranging from robotics to music generation.