I don't think this analysis matches the underlying implementation.
The width of the models is typically wide enough to "explore" many possible actions, score them, and let the sampler pick the next action based on the weights. (Whether a given trained parameter set will be any good at it, is a different question.)
The number of attention heads for the context is similarly quite high.
And, as a matter of mechanics, the core neuron formulation (dot product input and a non-linearity) excels at working with ranges.
The width of the models is typically wide enough to "explore" many possible actions, score them, and let the sampler pick the next action based on the weights. (Whether a given trained parameter set will be any good at it, is a different question.)
The number of attention heads for the context is similarly quite high.
And, as a matter of mechanics, the core neuron formulation (dot product input and a non-linearity) excels at working with ranges.