In contrast, linear RNNs have low computational complexity and are suitable for long-range modeling.

Graph neural networks (GNNs) have shown the power in graph representation learningfornumeroustasks.

Natural language-conditioned reinforcement learning (RL) enables agents to follow human instructions.

Multi-agent games allow sophisticated interactions between agents and environment. Feasible solutions may require non-trivial intra-agent coordination, which leads to substantially more complex strategies than the single-agent setting.

In many real-world scenarios, data to train machine learning models becomes availableovertime.

Foundation models have emerged as powerful tools across various domains including language, vision, and multimodal tasks.