Foundation models contain a wealth of information from their vast number of
training samples. However, most prior arts fail to extract this information in
a precise and efficient way for small sample sizes. In this work, we propose a
framework utilizing reinforcement learning as a control for foundation models,
allowing for the granular generation of small, focused synthetic support sets
to augment the performance of neural network models on real data classification
tasks. We first allow a reinforcement learning agent access to a novel context
based dictionary; the agent then uses this dictionary with a novel prompt
structure to form and optimize prompts as inputs to generative models,
receiving feedback based on a reward function combining the change in
validation accuracy and entropy. A support set is formed this way over several
exploration steps. Our framework produced excellent results, increasing
classification accuracy by significant margins for no additional labelling or
data cost.

使用强化学习作为对基础模型的控制的框架，通过生成小而专注的合成支持集来增强神经网络模型在真实数据分类任务上的性能，而无需额外的标记或数据成本。