Creative Priming: Decoupling Process from Product to Escape the Creative Paradox

Many of the most consequential advances promised by AI, from curing diseases and inventing new technologies to formulating new scientific theories, are fundamentally creative acts. Yet creativity is uniquely difficult to learn because it faces a creative paradox: learning to repeat past examples of creative success makes those specific examples more likely to recur, inhibiting the very creativity desired. This work argues that creativity is better modeled as an explicit process rather than as a property of individual artifacts, and proposes to learn the underlying generative principles behind the creative process. In particular, to escape the paradox, we introduce Creative Priming (CP), wherein a trained priming model generates a prompt-specific primer that assists a response model in being creative, explicitly decoupling the creative process from response generation. The priming model learns through GRPO, rewarding primers that lead to responses that are novel, high-quality, and diverse, thereby reinforcing the intermediate steps that yield creative responses without reinforcing any specific response. Evaluating CP on NoveltyBench and comparing against response models directly trained for the same properties, we find that priming produces a 39–300% relative increase in the number of responses that are simultaneously novel, high-quality, and semantically distinct. Additionally, although the primer is only trained to improve creativity for a small open-source model (Qwen3-8B), the creative principles it learns transfer to frontier API models, increasing the number of semantically distinct responses they generate by 4.5–10× and diversity by 2.5–4.6×, while maintaining or improving quality. CP thus shows that decoupling the creative process from the responses themselves offers a concrete escape from the creative paradox.