Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions. Recent advances in flow-matching have enabled population-level prediction of cellular responses. However, flow-matching training can fail to recover certain target distributions even within the model family, limiting its ability to capture cellular heterogeneity. We first prove that post-training can recover these distributions, then introduce PerturbCellRL, a reinforcement learning framework that post-trains single-cell perturbation generators using per-cell rewards. The central component is a gene-expression energy witness that translates population-level discrepancies into per-cell feedback. We further prove that this reward's policy gradient points toward better distributional alignment. Two complementary rewards, calibrated on real cells, penalize atypical expression profiles and insufficient pathway-level responses to perturbations. Across genetic and chemical perturbation benchmarks, PerturbCellRL substantially improves distributional alignment and recovers pathway enrichment patterns more faithfully. These results establish reward-guided post-training as an effective strategy for improving both distributional accuracy and biological fidelity in perturbation prediction.

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