AuthorMix proposes a lightweight, modular, and interpretable style transfer framework that allows for rapid training of specialized adaptation models for each new target using layer-wise adapter mixing via reinforcement learning. This approach improves meaning preservation and ranks first on the combined style-meaning score among all baselines, including GPT-5.1.