PaperAI Ethics & Safetyv1.0

Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

by OpenAI · free · Last verified 2026-03-17

This paper explores weak-to-strong generalization, a method for training a powerful AI model using supervision from a weaker one. It serves as an analogy for aligning superintelligent AI with human values. The research shows that strong models can learn beyond their weak supervisors and introduces techniques like auxiliary confidence loss to improve performance.

https://arxiv.org/abs/2312.09390 ↗

C—Below Average

Adoption: B+Quality: A+Freshness: B+Citations: FEngagement: F

Specifications

License: Open Access
Pricing: free
Capabilities: weak-to-strong-generalization, scalable-oversight, superalignment-research, model-alignment-techniques, auxiliary-confidence-loss, generative-model-supervision, bootstrapping-for-alignment, empirical-ai-safety
Integrations
Use Cases: [object Object], [object Object], [object Object], [object Object]
API Available: No
Tags: ai-safety, alignment, superalignment, weak-supervision, scalable-oversight, generalization, machine-learning, research-paper, large-language-models
Added: 2026-03-17
Completeness: 0.4%

Index Score

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Freshness

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