Beyond Retrieval and Fine-tuning: Learning from Scientific Execution Experience for Self-Improving Agents

Scientific execution experience often intertwines transferable strategies with context-specific constraints. Neither imitating successful trajectories nor accumulating external experience alone therefore guarantees effective transfer. We introduce BioMaster, a framework that learns from the same scientific execution experience through two complementary pathways: parameter learning and external experience reuse. BioMaster post-trains its model on filtered execution trajectories and distills task-specific methods into structured Research Units(RUs), which encode applicability conditions, execution dependencies, and verification evidence to support retrieval and adaptation at inference time. Failure diagnosis further guides targeted task construction and subsequent model updates. With the base model held fixed, BioMaster achieves the highest scores among the evaluated systems on STELLA Simple Set, Biomni-Eval1, and BioMystery. With the agent harness held fixed, its post-trained model improves BioMystery accuracy by 9.26 percentage points over the base model. Component analyses further support complementarity between parameter learning and experience reuse. Together, these findings highlight the value of jointly internalizing scientific experience into model parameters and structuring it as reusable methodological knowledge.