This is not my area of expertise, but my understanding is that it's a lot harder to learn on a sparse representation. You won't have many training examples for each input, so most of the neurons will be training on just a few examples.
The canonical example for RNNs would be NLP, and I think it's fairly standard to transform the input text from something sparse (e.g., a one-hot encoding of word IDs), to a dense vector embedding (e.g., word2vec). This page is pretty good:
In your case it's hard to say what exactly would be best without knowing more about your data. But maybe you could find some sort of vector embedding that would work for your data. Then you could capture some of its essential properties and train your RNN effectively.