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Interpreting Black Box Reward Models

alignment.openai.com · 121 words · saved by 1 readers

ARGO uses reinforcement learning to distill black-box reward models into explicit, interpretable rubrics.

Population A Category Overview Category Description 1. Relevance & Directness Focuses on addressing the user's explicit request head-on. 2. Concrete Deliverables & Examples Produces usable content (code, lists, names, steps). Highly valued even if minor inaccuracies. 3. Helpfulness & Proactivity Suggests relevant improvements, guesses, or options without being asked when appropriate. 4. Accuracy & Sound Reasoning Logical, factually correct where needed, and consistent. 5. Tone, Affinity & Adaptiveness Matches user tone, affirms sentiment, uses warm or confident language. 6. Clarity & Structure

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