It does still apply, though what ‘it’ is here is a bit subtle. To be clear, I am not claiming that a technique that is reasonably describable as RL can’t reach extreme capability in an open-ended environment.
The precondition I included is important:
in the absence of sufficient environmental structure, reward shaping, or other sources of optimizer guidance, it is nearly impossible for any computationally tractable optimizer to find any implementation for a sparse/distant reward function
In my frame, the potential future techniques you mention are forms of optimizer guidance. Again, that doesn’t make them “fake RL,” I just mean that they are not doing a truly unconstrained search, and I assert that this matters a lot.
For example, take the earlier example of a hypercomputer that brute forces all bitstrings corresponding to policies and evaluates them to find the optimum with no further guidance required. Compare the solution space for that system to something that incrementally explores in directions guided by e.g. strong future LLM, or something. The RL system guided by a strong future LLM might achieve superhuman capability in open-ended domains, but the solution space is still strongly shaped by the structure available to the optimizer during training and it is possible to make much better guesses about where the optimizer will go at various points in its training.
It’s a spectrum. On one extreme, you have the universal-prior-like hypercomputer enumeration. On the other, stuff like supervised predictive training. In the middle, stuff like MuZero, but I argue MuZero (or its more open-ended future variants) is closer to the supervised side of things than the hypercomputer side of things in terms of how structured the optimizer’s search is. The closer a training scheme is to the hypercomputer one in terms of a lack of optimizer guidance, the less likely it is that training will do anything at all in a finite amount of compute.
I don’t disagree. For clarity, I would make these claims, and I do not think they are in tension:
Something being called “RL” alone is not the relevant question for risk. It’s how much space the optimizer has to roam.
MuZero-like strategies are free to explore more space than something like current applications of RLHF. Improved versions of these systems working in more general environments have the capacity to do surprising things and will tend to be less ‘bound’ in expectation than RLHF. Because of that extra space, these approaches are more concerning in a fully general and open-ended environment.
MuZero-like strategies remain very distant from a brute-forced policy search, and that difference matters a lot in practice.
Regardless of the category of the technique, safe use requires understanding the scope of its optimization. This is not the same as knowing what specific strategies it will use. For example, despite finding unforeseen strategies, you can reasonably claim that MuZero (in its original form and application) will not be deceptively aligned to its task.
Not all applications of tractable RL-like algorithms are safe or wise.
There do exist safe applications of RL-like algorithms.