In few-shot learning, the strategy of refining meta-learned parameters uses meta-learners to learn an initial parameter set ($\theta_0$) on tasks similar to the target task. The few-shot task then further refines this $\theta_0$. In contrast to refining existing parameters, which uses a fixed initial $\theta_0$, this approach dynamically learns $\theta_0$ using a meta-learner.