Training a Scientific Reasoning Model for Chemistry

Understand

Reasoning models are large language models that emit a long chain-of-thought before answering, providing both higher accuracy and explicit reasoning for their response.

• A major question has been whether language model reasoning generalizes beyond mathematics, programming, and logic, where most previous work has focused.
• We demonstrate that reasoning models can be post-trained for chemistry without additional domain pretraining, and require substantially less data compared to contemporary domain-specific models.
• We report ether0, a 24B parameter LLM (based on Mistral-Small-24B) that can reason in natural language and respond with chemical structures.