Reinforcement Learning Fine-Tuning
Optimizing biomedical task-specific reasoning LLM with reinforcement learning
Case Studies
Real-world applications of our RL fine-tuning approaches in biomedical research.
Automated Gene Library Creation
We are interested in improving functional screen design in general, achieving super human performance by prompting a reasoning-capable LLM to directly propose a ranked list of genes tailored to the user's experimental context (disease focus, cell type, assay format).
To close the loop with empirical data, we compare the LLM-generated rankings against curated ground-truth gene sets drawn from ~400 published screens. The discrepancy between the model's list and the experimental reference is converted into a reward signal that favors lists with higher overlap and better ordering.
Results: With GRPO fine-tuning, AutoScreen delivers gene libraries that align more closely with empirical ground truth while continually improving through iterative feedback. Across all model sizes, reinforcement tuning produces consistent, dataset-wide improvements in list quality over purely supervised baselines.
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