To answer you second question,
As can be seen from the past recent developments, pre-trained masked-language models are more suitable for classification tasks or rather tasks that require bi-directional understanding. T5 has also show that it’s possible to achieve comparable results on classification tasks using text-to-text approach.
As for those two reformer models, they are causal LM’s trained for next token prediction so they might not perform well enough for classification. But I haven’t tried this myself so feel free to experiment.