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Weak-to-strong generalization
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: OpenAI
This is a key OpenAI paper directly relevant to the superalignment problem—how humans can maintain meaningful oversight of AI systems that may soon surpass human expertise across domains.
Metadata
Importance: 88/100blog postprimary source
Summary
This OpenAI research investigates whether a weak model (as a proxy for human supervisors) can reliably supervise and align a much more capable model. The key finding is that weak supervisors can elicit surprisingly strong generalized behavior from powerful models, but gaps remain—suggesting this approach is promising but insufficient alone for scalable oversight. The work frames superalignment as a core technical challenge for future AI development.
Key Points
- •Demonstrates that a weaker GPT model can supervise a stronger one, achieving better-than-weak performance—a proof of concept for scalable oversight.
- •Introduces the weak-to-strong generalization problem as an analogy for the challenge humans face in supervising superhuman AI systems.
- •Identifies a 'generalization gap' where strong models supervised by weak ones still underperform models trained with strong supervision.
- •Proposes techniques like bootstrapping and auxiliary confidence loss to narrow the gap between weak-supervised and strong-supervised performance.
- •Frames this research as foundational groundwork for OpenAI's superalignment initiative aimed at solving AI alignment within 4 years.
Review
The paper introduces a novel approach to the superalignment problem by exploring whether smaller, less capable AI models can effectively supervise and control more powerful models. This addresses a critical challenge in AI safety: how humans can maintain control over increasingly sophisticated AI systems that may soon exceed human intelligence. The researchers conducted experiments using GPT-2 to supervise GPT-4, achieving performance levels between GPT-3 and GPT-3.5, which suggests promising potential for scalable alignment techniques. While acknowledging current limitations, the study presents a proof-of-concept that naive human supervision might not suffice for superhuman models, and proposes methods like encouraging model confidence and strategic disagreement to improve generalization. The work opens up a crucial research direction for developing reliable oversight mechanisms as AI systems become more advanced.
Cited by 7 pages
| Page | Type | Quality |
|---|---|---|
| AI Safety Solution Cruxes | Crux | 65.0 |
| AI-Assisted Alignment | Approach | 63.0 |
| AI Alignment | Approach | 91.0 |
| RLHF | Research Area | 63.0 |
| Technical AI Safety Research | Crux | 66.0 |
| Weak-to-Strong Generalization | Approach | 91.0 |
| Optimistic Alignment Worldview | Concept | 91.0 |
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Weak-to-strong generalization \| OpenAI
December 14, 2023
[Safety](https://openai.com/news/safety-alignment/)
# Weak-to-strong generalization
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Justin Jay Wang × DALL·E
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We present a new research direction for superalignment, together with promising initial results: can we leverage the generalization properties of deep learning to control strong models with weak supervisors?
A core challenge for aligning future superhuman AI systems (superalignment) is that humans will need to supervise AI systems much smarter than them. We study a simple analogy: can small models supervise large models? We show that we can use a GPT‑2‑level model to elicit most of GPT‑4’s capabilities—close to GPT‑3.5‑level performance—generalizing correctly even to hard problems where the small model failed. This opens up a new research direction that allows us to directly tackle a central challenge of aligning future superhuman models while making iterative empirical progress today.
## The superalignment problem
We believe superintelligence—AI vastly smarter than humans—could be developed within the next ten years. However, we still do not know how to reliably steer and control superhuman AI systems. Solving this problem is essential for ensuring that even the most advanced AI systems in the future remain safe and beneficial to humanity.
We formed the [Superalignment team](https://openai.com/superalignment/) earlier this year to solve this problem of superintelligence alignment. Today, we are releasing the team’s first paper, which introduces a new research direction for empirically aligning superhuman models.
Current alignment methods, such as reinforcement learning from human feedback (RLHF), rely on human supervision. However, future AI systems will be capable of extremely complex and creative behaviors that will make it hard for humans to reliably supervise them. For example, superhuman models may be able to write millions of lines of novel—and potentially dangerous—computer code that would be very hard even for expert humans to understand.
Relative to superhuman AI models, humans will be “weak supervisors.” This is a core challenge for AGI alignment: how can weak supervisors trust and control substantially stronger models?
## Our setup
To make progress on this core challenge, we propose an analogy we can empirically study today: **can we use a smaller (less capable) model to supervise a larger (more capable) model?**
**A simple analogy for s
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