Train the AI system to be nice. There are a variety of things we can vary in how we train the AI:
Train using model internals OR using outputs.
The central internals-based things I’m imagining involve using the internals as a reward signal (e.g., like this). Calling “CoT” “internals” is sometimes reasonable (we might want to do process supervision on the CoT).
Vary how similar the distribution we’re training on is to the distribution that we care about.
For instance: do online training VS training in a toy domain.
Train using an imitation-based objective (SFT) OR an outcome-based objective (RL) OR train on declarative facts / stories (mid-training).
Train for good behavior or train against bad behavior. Training for good behavior might include training the AI to produce good looking reasoning, as in deliberative alignment.
Obviously, there’s a big question of how we get the labels / reward signal here, which should be studied. Especially if you’re doing untrusted monitoring.
We’ll also need to decide whether to use on or off policy data.
If we’re training on facts / stories stating that the AI is a nice guy: We can vary what the stories are, and how we instill the persona. For instance, we might add a bunch of irrelevant quirks to the persona, and train for those. We likely want to have the stories explain why the AI takes nice actions.
We might not directly train the policy, but instead train/prompt monitors, and orchestrate a control system. That is, we might ensemble potentially misaligned AI models into a hopefully mostly good AI system. When ensembling, we’ll likely do some amount of rejection sampling of bad actions, and also “factored cognition” / forcing the AI to solve problems that we don’t think it can sabotage. A large part of the problem here is figuring out how to do a good job of interrogating AI models to see if they’re sabotaging you (i.e., “debate” style techniques).
How can we make a nice AI system?
Here's a list of all the techniques I'm aware of.
Please let me know if I've missed any techniques!