Cross-Domain Recommendation (CDR) is a widely used approach for leveraging information from domains with rich data to assist domains with insufficient data. A key challenge of CDR research is the effective and efficient transfer of helpful information from source domain to target domain. Currently, most existing CDR methods focus on extracting implicit information from the source domain to enhance the target domain. However, the hidden structure of the extracted implicit information is highly dependent on the specific CDR model, and is therefore not easily reusable or transferable. Additionally, the extracted implicit information only appears within the intermediate substructure of specific CDRs during training and is thus not easily retained for more use. In light of these challenges, this paper proposes AutoTransfer, with an Instance Transfer Policy Network, to selectively transfers instances from source domain to target domain for improved recommendations. Specifically, AutoTransfer acts as an agent that adaptively selects a subset of informative and transferable instances from the source domain. Notably, the selected subset possesses extraordinary re-utilization property that can be saved for improving model training of various future RS models in target domain. Experimental results on two public CDR benchmark datasets demonstrate that the proposed method outperforms state-of-the-art CDR baselines and classic Single-Domain Recommendation (SDR) approaches. The implementation code is available for easy reproduction.