PerturbCellRL: Aligning Distributions and Grounding Biology via Post-Training Perturbation Generators

arXiv:2606.27752 · cs.LG · Submitted 2026-06-26 · Read on arXiv

cs.LG

Submitted: 2026-06-26

Updated: 2026-10-02

Terminology

• Verifier — 4× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes

Sources

• scDFM: Distributional Flow Matching Model for Robust Single-Cell Perturbation Prediction
• Training Diffusion Models with Reinforcement Learning
• Flow-GRPO: Training Flow Matching Models via Online RL
• DanceGRPO: Unleashing GRPO on Visual Generation
• MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE
• DiffusionNFT: Online Diffusion Reinforcement with Forward Process
• CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning
• Flow Matching Guide and Code
• CellFlux: Simulating Cellular Morphology Changes via Flow Matching
• The Cramer Distance as a Solution to Biased Wasserstein Gradients

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