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 ↗B
B—Above Average
Adoption: B+Quality: A+Freshness: B+Citations: AEngagement: 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
68Adoption
74
Quality
92
Freshness
70
Citations
80
Engagement
0