Meta-interpretive learning (MIL) is an approach within inductive logic programming (ILP) that aims to learn a program from a set of examples, metarules, and other background knowledge. MIL uses a depth-first, failure-driven strategy to explore appropriate clauses in the program space to generate programs. This mechanism, however, inevitably leads to the problem of repeated proofs for the same goals. A pruning strategy is proposed, utilizing Prolog’s built-in database mechanism to store failed goals and their corresponding error information, effectively preventing redundant proof processes. The accumulated error information serves as a guide to help the MIL system optimize and adjust its learning process in future iterations. The correctness of the pruning algorithm is proved, and the reduction in program space is calculated theoretically. The proposed method is applied to two existing MIL systems, Metagol and Metagol_AI, resulting in two new MIL systems Metagol_F and Metagol_{AI_F}. Empirical results from four different tasks demonstrate that the proposed strategy significantly reduces the time required to learn the same programs.