Traffic signal control (TSC) is one of the effective ways to mitigate traffic congestion, a growing problem causing significant economic loss to urban areas. In recent years, there is an increasing interest in using reinforcement learning (RL) in TSC since RL shows great potential in optimizing control policy for complex real-world traffic conditions. However, one concerning problem is the huge computing time/resources required to train the control policy for large-scale TSC. To address this problem, this paper proposes a method that utilizes a curriculum to help speed up the training process. Control policies of different single-intersection maps are learned first and then referenced to a large-scale map consisting of a certain number of different intersections. The preliminary evaluation demonstrates that our method achieves a jump-start compared to that of learning the target task from scratch.