Sample Efficient Adaptation

Sample-efficient adaptation focuses on improving the performance of machine learning models on new tasks or data distributions using minimal additional training data. Current research explores techniques like iterative refinement, error-driven learning, and test-time adaptation of input samples, often leveraging pre-trained models and employing lightweight neural networks or energy-based models to achieve this efficiency. These advancements are significant because they reduce the computational cost and data requirements for adapting models, enabling broader deployment in resource-constrained environments and facilitating faster adaptation to evolving data streams in various applications, including code generation, image classification, and reinforcement learning.