You could theoretically run the input twice, allowing the model to correlate later tokens with previous ones. It would fix the problem with not knowing what information to retain. A more complicated approach would train the RNN to request replaying some earlier data when needed.

A great thing about RNNs is they can easily fork the state and generate trees, it would be possible to backtrack and work on combinatorial search problems.

Also easier to cache demonstrations for free in the initial state, a model that has seen lots of data is not using more memory than a model starting from scratch.