Any temporal discounting other than temporal is provably inconsistent

The conditions of the proof are applicable only to reinforcement agents which, as a matter of architecture, are forced to integrate anticipated rewards using a fixed weighting function whose time axis is constantly reindexed to be relative to the present.

To recap, the idea is that it is the self-similarity property of exponential functions that produces this result - and the exponential function is the only non-linear function with that property.

All other forms of discounting allow for the possibility of preference reversals with the mere passage of time - as discussed here.

This idea has nothing to do with reinforcement learning.