Jinran Jin, Ryan Teehan, Jack Lu, and Mengye Ren

Preprint · 2026-10-27

Language models have distinct and recognizable writing styles, yet these writing styles are rarely articulated precisely. In this paper, we study whether a model's style can be described in words for another model to adapt and follow. To measure the fitness of a description, we ask independent evaluators to distinguish the pair of responses. Rather than coming up with an ad hoc prompt, our proposed framework, PrompTo, creates a search loop to optimize for the style prompt that confuses the evaluator. A reflector then takes in the reward and language feedback and generates a prompt candidate for the next iteration. The optimized prompts recover 74.8% of the source–target gap on average, against at most 15.2% for existing baselines. Moreover, we found style prompts optimized with one source model readily transfer to other source models.