PertMind: Eliciting Emergent Biological Reasoning in LLM via Reinforcement Learning on Cellular Perturbation Data

TL;DR - PertMind uses cellular perturbation measurements as reinforcement-learning rewards to improve an LLM’s biological reasoning without relying on extensive manually curated reasoning traces. The approach may turn growing experimental atlases into scalable training environments for biological foundation models.

• Combines trusted-trajectory supervised initialization with gene-, pathway-, and format-level reinforcement signals.
• Improves perturbation-response prediction in unseen cellular contexts while retaining general language capabilities.
• Transfers without task-specific post-training to reverse and double-perturbation reasoning, screen prioritization, and biological-process interpretation.
• Produces biological profiles useful for gene, cell, and donor representations across downstream tasks.