Mainly, minecraft isn’t actually out of distribution, LLMs still probably have examples of nice / not-nice minecraft behaviour.
Is this inherently bad? Many of the tasks that will be given to LLMs (or scaffolded versions of them) in the future will involve, at least to some extent, decision-making and processes whose analogues appear somewhere in their training data.
It still seems tremendously useful to see how they would perform in such a situation. At worst, it provides information about a possible upper bound on the alignment of these agentized versions: yes, maybe you’re right that you can’t say they will perform well in out-of-distribution contexts if all you see are benchmarks and performances on in-distribution tasks; but if they show gross misalignment on tasks that are in-distribution, then this suggest they would likely do even worse when novel problems are presented to them.
Is this inherently bad? Many of the tasks that will be given to LLMs (or scaffolded versions of them) in the future will involve, at least to some extent, decision-making and processes whose analogues appear somewhere in their training data.
It still seems tremendously useful to see how they would perform in such a situation. At worst, it provides information about a possible upper bound on the alignment of these agentized versions: yes, maybe you’re right that you can’t say they will perform well in out-of-distribution contexts if all you see are benchmarks and performances on in-distribution tasks; but if they show gross misalignment on tasks that are in-distribution, then this suggest they would likely do even worse when novel problems are presented to them.