Verizon media (VZM) native advertising serves billions of impressions daily, reaching a yearly run-rate of many hundred of million USD. Driving VZM native models for predicting advertise (ad) event probabilities, such as clicks and conversions, is OFFSET - a feature enhanced collaborative-filtering ( CF) based e vent prediction algorithm. The predicted probabilities are then used in VZM native auctions to determine which ads to present for each serving event. Dynamic creative optimization (DCO) is a new VZM native product that was launched recently and is gaining increasingly more attention from advertisers. The DCO product allows advertisers to provide several assets per each native ad attribute, creating a plurality of combinations for each DCO ad. Since different combinations may appeal to different crowds, it may be beneficial to present certain combinations more frequently than others to maximize revenue. Inspired by the success of our Carousel asset optimization product, we present a post-auction successive elimination based approach for ranking DCO combinations according to their measured click through rates (CTR). This reinforcement learning multi-arm bandit like solution was evaluated during an online beta test phase done with selected advertisers, showing 21.8% CTR and 21.5% revenue lifts over a control bucket serving all combinations uniformly at random. The good performance of our DCO product attracts advertisers and it already shows a yearly run-rate of several million USD in revenue.