PertMind: Eliciting Emergent Biological Reasoning in LLM via Reinforcement Learning on Cellular Perturbation Data
PertMind reorients cellular perturbation data into a reinforcement learning game where gene responses serve as rewards for biological reasoning. The system learns to predict gene expression changes based on cell lines and drug perturbations by optimizing a composite reward function that balances gene-level accuracy, pathway direction, and output format. This method enables the model to develop emergent reasoning abilities beyond its initial training.