lstm-learns-over-thousands-millions-timesteps

IN premise — entries/2026/06/21/wiki-Recurrent_neural_network-chunk-3.md

Created 2026-06-21T09:55:53+00:00

LSTM can learn dependencies across thousands or millions of time steps by allowing error to flow backward through unlimited virtual layers.

Dependents

These beliefs depend on this one:

• IN lstm-transformer-parallelism-tradeoff — The LSTM-to-Transformer transition traded the ability to learn dependencies across unlimited timesteps for massively parallel training, a sequential-depth-for-parallel-breadth exchange.
• OUT rnn-theoretical-power-practically-accessible — RNNs' theoretical computational power (Turing-completeness, context-sensitive language recognition) is practically accessible for sequence modeling tasks, given LSTM's ability to learn over thousands of timesteps.