TREACLE could be extended to handle a mixture of different task types by incorporating task-specific features into the state vector used for decision-making. Each task type could have its own set of features that capture the unique characteristics of that task. For example, for language generation tasks, features related to text complexity or language style could be included, while for translation tasks, language pairs and translation difficulty could be considered.
Additionally, the reinforcement learning policy in TREACLE could be trained on a diverse dataset that includes examples from various task types. By exposing the model to a wide range of tasks during training, it can learn to adapt its decision-making process to different types of queries. The policy could be designed to dynamically allocate the budget based on the perceived difficulty or resource requirements of each task type, ensuring that the overall budget is optimally distributed across tasks.
Furthermore, prompt strategies specific to each task type could be incorporated into the framework. Different prompts may be more effective for certain types of tasks, and TREACLE could learn to select the most appropriate prompt for each task based on its characteristics and requirements.