Demonstration selection plays a crucial role in the effectiveness of in-context learning (ICL) for large language models (LLMs). Extensive research has explored various retrieval strategies to identify informative demonstrations and improve downstream performance. Recently, the emergence of Jev, a decision-oriented language model capable of producing probabilistic judgments, offers a new perspective on evaluating demonstration utility. This motivates us to investigate a fundamental question: Can Jev improve demonstration retrieval for in-context learning? In this paper, we demonstrate that Jev can effectively enhance demonstration retrieval by directly assessing the utility of candidate demonstrations for a given query. However, applying Jev to large-scale retrieval introduces substantial computational overhead, as each candidate requires an additional decision-making inference. To address this limitation, we propose a simple yet effective knowledge distillation approach that transfers Jev's utility estimation capability to a lightweight multilayer perceptron (MLP). By learning to approximate Jev's probabilistic judgments, the distilled model enables efficient demonstration ranking while maintaining competitive retrieval performance. Our study highlights the potential of decision-oriented language models for demonstration selection and provides an efficient framework for integrating their decision-making capabilities into ICL.

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