Compositional Task

Compositional tasks challenge artificial intelligence systems to solve problems by combining simpler sub-tasks, mirroring human cognitive abilities. Current research focuses on improving large language models (LLMs) and reinforcement learning (RL) agents' performance on these tasks, exploring techniques like recursive tuning, knowledge distillation, and specialized prompting strategies (e.g., skills-in-context prompting) to enhance their compositional reasoning capabilities. These efforts aim to overcome limitations in current architectures, such as Transformers, which struggle with efficient function composition and generalization to unseen combinations of sub-tasks. Success in this area would significantly advance AI's ability to handle complex, real-world problems requiring multi-step reasoning and flexible adaptation.