When deploying models that learn from new tasks with scarce data, routing samples intelligently based on task similarity prevents negative interference while maximizing knowledge reuse across overlapping tasks.
This paper tackles continual learning when tasks have limited data and may overlap unpredictably. The authors propose an adaptive mixture-of-experts system that learns which tasks are similar and routes data accordingly, using two key techniques: gradually introducing task-specific prompts over time and identifying which samples fit existing patterns versus need new ones.