Conformant planning is usually transformed into a search problem in the space of belief states. In this paper, a method which can improve efficiency of planning by reducing the nondeterministic degree of belief states is proposed. An enforced hill-climbing algorithm for reducing belief states is presented first. Then, the method of Conformant planning based on reducing belief states is proposed. A planner named CFF-Lite implements this idea and is designed. The planner includes two phases of enforced hill-climbing which are used to reduce belief states and search the goal respectively. Before the search phase, the initial belief state is reduced furthest to an intermediate state which is much more deterministic. Next, the precision of heuristic information is improved and the heuristic search phase is performed. Experimental results show that the CFF-Lite planner can decrease the difficulty of Conformant planning problems by reducing belief states and with most of the test problems this method outperforms Conformant-FF in both planning efficiency and planning quality.