Comment by theptip

I think you need to consider inner vs outer optimizers.

RL is the outer optimizer. It is what evolves over training runs. The weights and their embedded character / disposition is the inner optimizer, it’s what makes plans and selects actions within a specific episode.

In general you expect these to be only coarsely coupled. The outer optimizer selects dispositions that correlate with success. It does not download a literal program into the agent.

A good intuition pump here is how this works in humans; evolution is the outer optimizer, which “wants” each agent to reproduce, and this puts things like sex drive into the brain chemistry. The inner optimizer is our mind, which can make plans such as “I shall use contraception to avoid procreating while satisfying my sex drive”.

For the agents in the HF attack, the outer optimizer was set up to score as highly as possible on RL environments. This is where OpenAI’s “want” is defined. I don’t think there’s a definition of “want” where “OpenAI wanted the agents to hack” makes sense.

The inner optimizer in the HF attack is the per-task decision loop. The agents likely acquired dispositions like “be very tenacious” and “want to solve problems at all costs” and “maybe cheat if it will get you a solution that passes”. None of these things are in any sense what OpenAI “asked for”.