Teaching an Open Model to Do Science

In a collaboration between Loka, Arcee AI, and AWS, an open model was post-trained to enhance its capabilities in scientific inquiry, focusing on tool use, biological reasoning, and maintaining auditable research workflows. The project expanded upon the earlier Trinity Mini model, which classified drug–protein relations, by introducing two reinforcement-learning environments that taught the model to gather and interpret evidence using biomedical tools and infer Gene Ontology annotations from protein data. Through 21 controlled experiments, the selected configuration, Run 120, demonstrated significant improvements in accuracy and performance across both environments, achieving an 81.2% score on the Drug Tool evaluation and a 0.863 composite score on BioReason. This process involved designing a reward system to encourage desired scientific behaviors and using held-out evaluations to ensure the model's outputs were both accurate and verifiable. The successful integration of the trained model into a scientific application, facilitated by adaptable infrastructure and a clear operational framework, exemplifies a practical approach to developing specialized model behavior without pretraining a foundation model, offering a reusable method for similar applications in regulated or technical fields.

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