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The introduction of large-scale, genome-wide, single-cell perturbation datasets provides the chance to learn a full gene regulatory network in the relevant cell line. However, existing gene regulatory network inference methods either fail to scale or do not explicitly leverage the interventional nature of this data. In this work, we propose an algorithm that builds upon GRNBoost by adding an additional step that complements its performance in the presence of labeled, single-gene interventional data. Applying BetterBoost to the CausalBench Challenge, we demonstrate its superiority over the baseline methods in inferring gene regulatory networks from large-scale single-cell perturbation datasets. Notably, BetterBoost exhibits significantly improved performance when non-zero fractions of labeled interventions are available, highlighting the effectiveness of our approach in leveraging interventional data for accurate gene regulatory network inference.