Great question, thanks. tldr it depends what you mean by established, probably the obstacle to establishing such a thing is lower than you think.
To clarify the two types of phase transitions involved here, in the terminology of Chen et al:
...4. Goals misgeneralize out of distribution.
See: Goal misgeneralization: why correct specifications aren't enough for correct goals, Goal misgeneralization in deep reinforcement learning
OAA Solution: (4.1) Use formal methods with verifiable proof certificates[2]. Misgeneralization can occur whenever a property (such as goal alignment) has been tested only on a subset of the state space. Out-of-distribution failures of a property can only be ruled out by an argument for a universally quantified statement about that property—but such arguments can in fact be
I think you’re directionally correct; I agree about the following:
However, I think maybe my critical disagreement is that I do think probabilistic bounds can be guaranteed sound, with respect to an uncountable model, in finite time. (They just might not be tight enough to...
In your example there are many values of the parameters that encode the zero function (e.g. θ1... (read more)