Decision theory likes to put its foot down on a particular preference, and then ask what follows. During inference, a pre-trained model seems to be encoding something that can loosely be thought of as (situation, objective) pairs. The embeddings it computes (residual stream somewhere in the middle) is a good representation of the situation for the purpose of pursuing the objective, and solves part of the problem of general intelligence (being able to pursue ~arbitrary objectives allows pursuing ~arbitrary instrumental objectives). Fine-tuning can then essentially do interpretability to the embeddings to find the next action useful for pusuing the objective in the situation. System prompt fine-tuning makes specification of objectives more explicit.
This plurality of objectives is unlike having a specific preference, but perhaps there is some “universal utility” of being a simulator that seeks to solve arbitrary decision problems given by (situation, objective) pairs, and to take intentional stance on situations that don’t have an objective explicitly pointed out, eliciting an objective that fits the situation, and then pursuing that. With an objective found in environment, this is similar to one of the things corrigibility does, adopting preference that’s not originally part of the agent. And if elicitation of objectives for a situation can be made pseudokind, this line of thought might clarify the intuition that the concept of pseudokindness/respect-for-boundaries has some naturality to it, rather than being purely a psychological artifact of desperate search for rationalizations that would argue possibility of humanity’s survival.
Decision theory likes to put its foot down on a particular preference, and then ask what follows. During inference, a pre-trained model seems to be encoding something that can loosely be thought of as (situation, objective) pairs. The embeddings it computes (residual stream somewhere in the middle) is a good representation of the situation for the purpose of pursuing the objective, and solves part of the problem of general intelligence (being able to pursue ~arbitrary objectives allows pursuing ~arbitrary instrumental objectives). Fine-tuning can then essentially do interpretability to the embeddings to find the next action useful for pusuing the objective in the situation. System prompt fine-tuning makes specification of objectives more explicit.
This plurality of objectives is unlike having a specific preference, but perhaps there is some “universal utility” of being a simulator that seeks to solve arbitrary decision problems given by (situation, objective) pairs, and to take intentional stance on situations that don’t have an objective explicitly pointed out, eliciting an objective that fits the situation, and then pursuing that. With an objective found in environment, this is similar to one of the things corrigibility does, adopting preference that’s not originally part of the agent. And if elicitation of objectives for a situation can be made pseudokind, this line of thought might clarify the intuition that the concept of pseudokindness/respect-for-boundaries has some naturality to it, rather than being purely a psychological artifact of desperate search for rationalizations that would argue possibility of humanity’s survival.