Oracle-Free Autonoumous Post-Training for Chemistry via Dual-Stream Data Synthesis

Large language models have shown strong potential in chemistry, while employing them for long-tail chemistry tasks still requires domain adaptation, which remains labor-intensive. Recent AI-for-AI agents have demonstrated success in automating this process across domains such as programming and mathematics. However, these methods rely on rich training data and ready-made evaluation oracles, which long-tail chemistry rarely offers. To this end, we propose AutoChem, an oracle-free agentic system that starts with a single chemistry task description, builds its own training data and evaluation oracle, and post-trains a model to meet the task's requirements. At its core is a dual-stream data synthesis stage, in which two agents independently build two data synthesis engines, so that either stream can train the model while the other grades it. AutoChem then post-trains on the samples from one stream and evaluates on the other, so that gains reflect learned chemistry rather than leakage from the training engine into the test set. Extensive experiments across three scientific discovery tasks and eleven tasks from four chemistry benchmarks demonstrate AutoChem's effectiveness, generalization, and applicability.