Offline-to-Online Creative Optimization with Generative Models and Adaptive Testing
Researchers propose a new workflow that uses predictive models to bridge the gap between offline generative AI and online A/B testing for advertising.
- Generative models can be constrained by predictive critics to improve ad performance.
- Offline predictive models can simulate online A/B test outcomes to save costs.
- The workflow reduces the number of failed experiments needed to find winning creatives.
The core challenge in modern digital advertising is no longer just generating content, but knowing which content will actually perform. While generative models can create endless variations of an ad, testing them all through live A/B experiments is prohibitively expensive and slow.
This research introduces an offline-to-online workflow designed to solve this bottleneck. By training a predictive model on historical experimental data, the system acts as an inference-time critic. This critic evaluates and ranks generated candidates before they ever reach a live user.
By using this predictive model to guide the selection process, companies can significantly increase the efficiency of their creative testing cycles. This approach ensures that the limited slate of ads chosen for live testing has a much higher probability of success compared to random selection.
Provides a framework for integrating predictive evaluation into generative pipelines.
Enables more efficient marketing spend by reducing wasted testing on low-performing assets.
Offers a practical application of offline reinforcement learning and predictive modeling in industry.
- A/B testing
- A randomized experimentation process used to compare two versions of a variable to determine which performs better.
- Inference-time critic
- A model used during the generation process to evaluate and refine the quality or relevance of the output.
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