OpenAI Superalignment Fast Grants

OpenAI has launched a $10 million grants program to support technical research aimed at ensuring the safety and alignment of superhuman AI systems. This initiative addresses the critical challenge of how humans can steer and trust AI systems that possess capabilities far exceeding human intelligence.

The Challenge of Superhuman AI Alignment

Aligning superhuman AI systems presents fundamentally different technical challenges than current AI alignment. While current systems are typically aligned using reinforcement learning from human feedback (RLHF), this method relies on human supervision, which becomes insufficient when AI capabilities surpass human understanding.

Superhuman AI systems may exhibit complex and creative behaviors that humans cannot fully evaluate. For example, OpenAI notes that if a superhuman model generates a million lines of highly complex code, human reviewers would be unable to reliably determine if that code is safe or dangerous to execute. Consequently, the central technical problem is determining how humans can maintain control and trust over systems significantly smarter than themselves.

Superalignment Fast Grants Program Details

In partnership with Eric Schmidt, OpenAI is providing $10 million in funding to academic labs, nonprofits, and individual researchers. The program is designed to attract both experienced alignment researchers and new entrants to the field.

Funding Tiers and Eligibility

• General Grants: Grants ranging from $100,000 to $2 million are available for individual researchers, nonprofits, and academic labs.
• OpenAI Superalignment Fellowship: A one-year fellowship for graduate students providing a $75,000 stipend and $75,000 in compute and research funding (totaling $150,000).
• Experience Requirements: No prior experience in AI alignment is required to apply.

Priority Research Directions

OpenAI has identified several key technical areas where they are seeking breakthroughs in alignment and safety:

Weak-to-Strong Generalization

This research focuses on how strong models can generalize from the supervision of "weak" supervisors (humans). The goal is to understand and control how a superhuman model learns from feedback provided by a less capable entity.

Interpretability

Interpretability research aims to understand the internal mechanisms of AI models. A primary application of this research is the development of tools such as "AI lie detectors" to verify model honesty.

Scalable Oversight

Scalable oversight involves using AI systems to assist human operators in evaluating the outputs of other AI systems, particularly when performing complex tasks that exceed human individual capacity.

Additional Research Areas

OpenAI is also interested in funding research regarding:

• Honesty
• Chain-of-thought faithfulness
• Adversarial robustness
• Evaluations and testbeds