Part IV

A model can only practice a workflow it can actually perform, fail at, and retry. So we rebuilt the catalog workflow as a digital twin of an e-commerce platform: a simulated environment with the same listings, the same tools, and the same stakes as the real thing. The raw material is the Amazon Berkeley Objects dataset of real product images and listing data, which we turned into 177,767 review episodes: one listing each, with image, title, description, claimed brand, and region. Into that stream we planted controlled policy cases, mismatched images, conflicting brand claims, and deliberately legitimate claims as hard negatives, so every episode has a known correct answer to score against.

Inside the twin, the model works the way an analyst would. It can search a taxonomy of roughly 13,000 categories, check whether a brand is registered and protected, and retrieve the required attributes for a chosen category; then it must commit a category, the attributes, and a policy decision. A scorer grades every episode, rewarding correct outputs and penalizing missed violations, unsupported attributes, invalid categories, and wasted tool calls. The penalties encode business priorities directly: a missed violation costs 7× more than a false alarm.