Google DeepMind 论文：模拟环境 RL 训练提升 Gemini 3.1 Flash-Lite 精神科问诊能力

Rohan Paul· @rohanpaul_ai · X·2026-10-31 23:23· 1 小时前AI 评分64

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Google DeepMind 论文用多轮 RL 在模拟病人环境中训练 Gemini 3.1 Flash-Lite，提升其精神科问诊能力。32 名精神科医生扮演病人、15 名盲评转录，训练后模型在 60 组配对案例中 76.7% 胜过同提示的基线模型；256 个对抗案例中急性风险漏升级率从 30.5% 降至 12.9%，但约 1/8 的对抗性危机仍未升级。

正文 · 原文

New Google DeepMind paper shows that training a model with RL on simulated patients made it a better psychiatric intake interviewer, as judged by psychiatrists.

training on simulated psychiatric intakes cut missed escalations of acute risk from 30.5% to 12.9% in adversarial cases.

In psychiatry, the intake interview is a main diagnostic tool, and key facts often surface only through trust and careful questions. Most conversational medical AI evaluations have left psychiatric cases out.

They trained Gemini 3.1 Flash-Lite with multi-turn RL against simulated patients who reveal key facts only after rapport and targeted questions. Then 32 psychiatrists role-played patients, while 15 others graded the transcripts blind.

The trained agent beat its identically prompted base model in 76.7% of 60 matched case pairs. On 256 adversarial cases, it failed to escalate acute risks 12.9% of the time, down from 30.5%.

If you build health agents, train them on simulated conversations, but keep clinicians supervising, since about 1 in 8 adversarial crises went unescalated.

– arxiv. org/abs/2610.40476

Title: "Training language models in simulation for psychiatric intake"

来源：Rohan Paul · x.com