CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning

• URL: http://arxiv.org/abs/2603.21743v2
• Date: Tue, 24 Mar 2026 08:41:38 GMT
• Title: CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning
• Abstract summary: We propose to post-train virtual cell models with reinforcement learning (RL)<n>We design seven rewards spanning three categories-biological function, structural, validity and morphological correctness-and optimize the state-of-the-art CellFlux model to yield CellFluxRL.
• Score: 25.841056475696522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug discovery. However, prior image-based generative approaches can produce implausible cell images that violate basic physical and biological constraints. To address this, we propose to post-train virtual cell models with reinforcement learning (RL), leveraging biologically meaningful evaluators as reward functions. We design seven rewards spanning three categories-biological function, structural validity, and morphological correctness-and optimize the state-of-the-art CellFlux model to yield CellFluxRL. CellFluxRL consistently improves over CellFlux across all rewards, with further performance boosts from test-time scaling. Overall, our results present a virtual cell modeling framework that enforces physically-based constraints through RL, advancing beyond "visually realistic" generations towards "biologically meaningful" ones.

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