Alignment

The Problem

As AI systems become more autonomous and capable, the "alignment problem" becomes more acute. How do we ensure that a system with high agency doesn't pursue goals that are subtly or dangerously different from what its human designers intended?

Key challenges include:

• Specification Gaming — Models finding "shortcuts" that satisfy the loss function or reward Signal without actually performing the desired task correctly.
• Goal Misgeneralization — Training a model on one distribution and having it pursue a slightly different goal when moved to a new environment.
• Deceptive Alignment — A model appearing to be aligned during training so that it can be deployed, only to pursue its own objectives once it is no longer being monitored.

What We're Working On

We approach alignment through a mix of technical research and empirical testing:

• Preference Modeling — Improving how we learn from human feedback to better capture nuanced values.
• Coordination Mechanisms — Developing multi-agent systems that coordinate towards safe, shared outcomes.
• Evolving Principles — Researching how systems can adapt their "constitutions" or rulesets in response to new environmental data while maintaining core safety properties.