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
related reading
- AI Model & API Providers Analysis | Artificial Analysisartificialanalysis.ai
- The Shape of AI | UX Patterns for Artificial Intelligence Designshapeof.ai
- Parsed | Custom, interpretable AI systems that continuously learnparsed.com
- Goodfire AIgoodfire.ai
- Datacurve | The data engine for frontier AIdatacurve.ai
- Hume AI - The AI toolkit for voice and emotionhume.ai
- Contra Labs - Powered by Contracontralabs.com
- PromptLayer — Prompt Management, Evals & Observabilitypromptlayer.com
- Cookbookcookbook.openai.com
- Vals AIvals.ai
- Conceptual Reasoning Indexconceptualreasoning.ai
- Neuronpedianeuronpedia.org