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G-CARL: Checklist-Aligned Reward Learning for Grounded Medical AI Agents

This story is from 2026-08-23. It is preserved in the archive; the latest stories are on the live feed.

Medical AI agents face a dual constraint problem. They must stay grounded in clinical evidence while adapting explanations to individual patient context. Standard RLHF optimizes for a single holistic reward signal, which lets the model trade factuality for fluency or vice versa. G-CARL (Grounded Ch…

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  1. 2026-08-23 10:06 · DEV Community — Machine Learning
    G-CARL: Checklist-Aligned Reward Learning for Grounded Medical AI Agents

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