Title: Zero-Order Sharpness-Aware Minimization
Primary area: optimization
Keywords: Zero-order optimization, Sharpness-aware minimization, Prompt tuning, Gradient estimation, Generalization bounds

Abstract: Prompt learning has become a key method for adapting large language models to specific tasks with limited data. However, traditional gradient-based optimization methods for tuning prompts are computationally intensive, posing challenges for efficiency. We introduce ZOSA (Zero-Order Sharpness-Aware Minimization), a novel optimization framework that integrates zero-order optimization with sharpness-aware minimization to enhance prompt tuning. ZOSA employs Rademacher perturbation vectors to estimate gradients without requiring backpropagation. By incorporating sharpness-aware principles, it targets flat minima in the loss landscape, improving generalization. An adaptive learning rate, guided by loss variability, further ensures stable convergence. Experiments on few-shot learning tasks, such as text classification and natural language inference, show that ZOSA significantly outperforms existing methods. With its theoretical foundation and computational efficiency, ZOSA offers a practical solution for prompt-based learning in resource-limited settings.

TL;DR: ZOSA is a novel zero-order sharpness-aware minimization framework that integrates Rademacher perturbations and adaptive loss-variance scaling to enable efficient prompt tuning for large language models.