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AI CURATED HOT STORY · 10/31/2026

DeepMind Paper: RL on Simulated Patients Improves Psychiatric Intake

1 reports archived1 independent sourcesupdated 10/31/2026, 23:23:00
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A new Google DeepMind paper demonstrates that training Gemini models with multi-turn reinforcement learning on simulated patients significantly improves psychiatric intake performance. The method reduced missed acute risk escalations from 30.5% to 12.9% and outperformed the base model in 76.7% of matched cases.

LATEST/A new Google DeepMind paper demonstrates that training Gemini models with multi-turn reinforcement learning on simulated patients significantly improves psychiatric intake performance. The method reduced missed acute risk escalations from 30.5% to 12.9% and outperformed the base model in 76.7% of matched cases.

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  1. Rohan Paul (@rohanpaul_ai)T2·78 pts

    DeepMind Paper: RL on Simulated Patients Improves Psychiatric Intake

    • Multi-turn RL training with simulated patients drastically reduces missed acute risk escalations in psychiatric intakes (from 30.5% to 12.9%).
    • The trained model outperforms the prompt-only baseline in 76.7% of matched case pairs.
    • Despite improvements, ~1 in 8 adversarial crises remain unescalated, highlighting the need for clinician supervision.