We study the task of persona-steered text generation, where models must generate text that reflects the distribution of views that an individual fitting a persona could have. We find models are worse at representing multifaceted personas whose dimensions are incongruous with each other, and that preference-based fine-tuning improves LLM steerability at the cost of diversity.