A good advisor comforts someone in crisis, challenges someone in denial, and gives practical guidance on a logistical question. We ask whether large language models (LLMs) vary their approach in this way. We define five advisory personas based on how comforting or challenging a reply is, and whether it supports a realistic understanding of the situation and informed decision-making. Across 1,280 Reddit situations in 14 advice contexts, human replies vary systematically, while three frontier LLMs default to a standard reassuring persona. We call this narrowing persona collapse. Training models to imitate human replies broadens persona use but often turns support into enabling, and challenge into cynicism. In a blinded study (N=178), expert raters judge these replies less favorably than the baseline model's on both immediate preference and potential longer-term help. We compare 13 training methods to investigate how models can learn different approaches without sacrificing advice quality. The best combination of persona fit and advice quality comes from learning from model-written replies guided by human examples, followed by reinforcement learning with quality feedback. In a second human study (N=159), raters place this model above the baseline for anticipated longer-term help. We find that models encode information about when to comfort, challenge, or give practical guidance, but their replies do not reliably reflect these distinctions. Our training narrows this representation-action gap, while activation steering alone does not reliably improve advice quality. Better LLM advice requires learning both which persona fits a situation and how to express it constructively.

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