Before the Furnace: Training LLM Agents for Inorganic Retrosynthesis

Inorganic retrosynthesis connects materials discovery with synthesis planning by identifying precursor sets for a target composition. Effective recommendations require models to acquire evidence beyond composition and use it to assess and revise precursor candidates. We introduce PreWeave, an LLM agent trained for inorganic precursor recommendation through tool interaction. We train its interaction policy through supervised fine-tuning on distilled trajectories followed by agentic reinforcement learning. The policy learns to select tools and use their feedback to guide subsequent precursor decisions. Agentic training improves precursor-set Pass@1 accuracy from 53.81% for the base agent to 73.02%. PreWeave achieves competitive performance with task-specific models and tool-augmented frontier LLMs. Further analyses show that training makes reference-matching recommen- dations more consistent across runs and shifts tool use toward candidate construction, with retrieval contributing most to accuracy among the available tools. We will release tools, interaction trajectories, and training and evaluation code to support further research on agent policy training for materials synthesis.