PerturbCellRL: Aligning Distributions and Grounding Biology via Post-Training Perturbation Generators
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-lev...
| Authors | Dongxia Wu, Mingyu Li, Yuhui Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 26 June 2026 |
| Last updated at source | 2 October 2026 |
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