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Notes -
In context learning is not equivalent to training. There are certain model behaviors that are easily attainable through finetuning which cannot be achieved through in context learning. For example a properly safety tuned model will not output toxic content no matter how any of those two million tokens you use to insist that it should. The fact is that the model's ability to reason with in-context information versus embedded information is fundamentally different, so you will not be able to reach agi with just an infinite context window.
Of course I'm not saying you need agi to beat pokemon and the current state of the art should be capable of doing it if some minor adjustments are made.
You can just look at Pliny jailbreaking SOTA models safety tuned to the max in a manner of hours or days? Do you think that definitionally, they aren't "properly" safety tuned because they're vulnerable to jailbreaks?
I don't see an empirical basis for that claim. I am agnostic on whether infinite (or just extremely large) context windows will suffice, especially when attached to a capable base model.
Even without it, a model that has fixed weights, a limited context window, but is otherwise highly intelligent, could approximate the benefits of online learning by coding and training a better version of itself. At that point you have recursive self improvement, though we're not entirely there yet (labs are increasingly using ever larger fractions of LLM written code for their internal work).
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