The principles of the instruction hierarchy can be applied to other AI systems beyond language models, such as multimodal models or reinforcement learning agents, by adapting the concept of instruction prioritization to suit the specific characteristics and requirements of these systems.
For multimodal models, the instruction hierarchy can be extended to consider instructions from different modalities, such as text, images, and audio, and prioritize them based on their relevance and importance to the task at hand. This can help the model effectively integrate information from multiple sources and make informed decisions across different modalities.
In the case of reinforcement learning agents, the instruction hierarchy can be used to define a clear hierarchy of goals and objectives for the agent to follow. By assigning priorities to different tasks or actions, the agent can learn to focus on high-priority goals while considering lower-priority tasks as secondary objectives. This can improve the efficiency and effectiveness of the agent's decision-making process and help it achieve optimal performance in complex environments.