Online learning in RNNs doesn't require sophisticated credit assignment algorithms—proper gradient normalization with immediate derivatives is sufficient and dramatically more memory-efficient.
Recurrent networks can learn online using simple immediate derivatives instead of expensive backpropagation-through-time. The key insight: the hidden state naturally carries temporal information forward, so you just need proper gradient normalization and avoid stale memory traces. This approach matches or beats complex algorithms while using 1000x less memory.