Distillation, Self-Training, and the Fragility of Learned Chemical Reasoning
Automating synthesis planning for small-molecule drug design requires chemical reasoners that are accurate as well as open to inspection and adaptation, yet frontier language models remain unreliable at predicting reaction outcomes and difficult to deploy, while small open-weight models lack the necessary domain reasoning. We show that frontier supervision can serve as a one-time bootstrap for open models on forward reaction prediction. Our models, Resonance, distill an open-weight teacher's reasoning traces and then learn from their own verified outputs, turning a small base model with near-zero accuracy into a capable reaction predictor, while a stronger student surpasses its teacher th...
| Authors | Anagha Aneesh, Sebastian Ibarraran, Frank Hu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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