Model predictive task sampling for efficient and robust adaptation.

Area of Science:

• Artificial Intelligence
• Machine Learning

Background:

• Foundation models and generalist policies require robust adaptation learning techniques like meta-training and supervised finetuning.
• Prioritizing challenging tasks is crucial for adaptation robustness, especially under distribution shifts.
• Evaluating task difficulty is computationally expensive, hindering effective adaptation.

Purpose of the Study:

• To introduce Model Predictive Task Sampling (MPTS), a novel framework for active task selection.
• To bridge task space and adaptation risk distributions for efficient and robust adaptation.
• To amortize the cost of task difficulty evaluation using a lightweight generative model.

Main Methods:

• MPTS employs a lightweight generative model to predict task-specific adaptation risk.
• The framework provably ranks task difficulties, enabling prioritized sampling.
• MPTS seamlessly integrates with zero-shot, few-shot, and supervised finetuning approaches.

Main Results:

• MPTS significantly enhances adaptation robustness for tail risk and out-of-distribution tasks.
• The method demonstrates improved learning efficiency compared to existing subset selection techniques like CVaRα.
• Experiments in pattern recognition and sequential decision-making validate MPTS's effectiveness.

Conclusions:

• MPTS offers an efficient and effective solution for active task selection in adaptation learning.
• The framework improves the robustness and efficiency of foundation models and generalist policies.
• MPTS represents a significant advancement in handling distribution shifts and challenging tasks.