Recurrent networks process sequence positions through a chain of hidden states. Attention creates direct, content-dependent connections between positions, changing both the learning path and the computational trade-offs.
Shorter information paths
In a recurrent model, information from an early token passes through many sequential updates before reaching a distant token. Self-attention can connect those positions in one layer, which helps models learn long-range dependencies.
Parallel training
Tokens in a transformer layer can be processed together once their representations are available. This maps well to modern accelerators. Standard full attention, however, compares every position with every other position and therefore becomes expensive for long contexts.
Architecture is a trade-off
Recurrence remains useful for streaming and bounded-memory settings. Attention is not automatically better for every sequence. The task, context length, latency target, hardware, and data regime determine whether full attention, sparse attention, recurrence, or a hybrid is appropriate.
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Adapted from The AI Algorithms Compendium, published by Choral Software. Article text and original examples © 2026 Choral Software. All rights reserved.