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Range query in Peer-to-Peer networks based on Distributed Hash Table (DHT) is still an open problem. The traditional way uses order-preserving hashing functions to create value indexes that are placed and stored on the corresponding peers to support range query. The way, however, suffers from high index maintenance costs. To avoid the issue, a scalable blind search method over DHTs - recursive partition search (RPS) can be used. But, RPS still easily incurs high network overhead as network size grows. Thus, in this paper, a learning-aware RPS (LARPS) is proposed to overcome the disadvantages of two approaches above mentioned. Extensive experiments show LARPS is a scalable and robust approach for range query, especially in the following cases: (a) query range is wide, (b) the requested resources follow Zipf distribution, and (c) the number of required resources is small.