Speaker-independent　speech　separation　has　achieved　remarkable　performance　in　recent　years　with　the　development　of　deep　neural　network　(DNN).　Various　network　architectures,　from　traditional　convolutional　neural　network　(CNN)　and　recurrent　neural　network　(RNN)　to　advanced　transformer,　have　been　designed　sophistically　to　improve　separation　performance.　However,　the　state-of-the-art　models　usually　suffer　from　several　flaws　related　to　the　computation,　such　as　large　model　size,　huge　memory　consumption　and　computational　complexity.　To　find　the　balance　between　the　performance　and　computational　efficiency　and　to　further　explore　the　modeling　ability　of　traditional　network　structure,　we　combine　RNN　and　a　newly　proposed　variant　of　convolutional　network　to　cope　with　speech　separation　problem.　By　embedding　two　RNNs　into　basic　block　of　this　variant　with　the　help　of　dual-path　strategy,　the　proposed　network　can　effectively　learn　the　local　information　and　global　dependency.　Besides,　a　four-staged　structure　enables　the　separation　procedure　to　be　performed　gradually　at　finer　and　finer　scales　as　the　feature　dimension　increases.　The　experimental　results　on　various　datasets　have　proven　the　effectiveness　of　the　proposed　method　and　shown　that　a　trade-off　between　the　separation　performance　and　computational　efficiency　is　well　achieved.