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How self-attention and RNNs process a sequence
Recurrent neural networks pass information through state
An RNN processes a sequence position by position. At each step, it combines the current input with a hidden state carried forward from the previous step. This gives the model a way to represent earlier context, but computation at a position depends on the preceding state.
That dependency limits parallel computation across positions within one training example: the next state cannot be calculated until the previous one is available. How well information persists over long distances depends on the recurrent design and the state the model learns.
Self-attention relates positions directly
Self-attention lets each position draw information from other positions in the sequence. In a Transformer, position representations can be calculated concurrently during training, rather than waiting for one position’s recurrent state before processing the next. The original Transformer paper describes its architecture as based on attention, without recurrence or convolution (Vaswani et al., “Attention Is All You Need” (2017)).
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Direct attention can connect arbitrary positions in a constant number of operations in the paper’s comparison, whereas recurrent information must pass through successive state updates. The paper also notes a possible effective-resolution cost for attention; fewer steps between positions do not mean every task or representation is automatically better.
Why are Transformers easier to train in parallel?
During training, a Transformer can calculate representations for multiple sequence positions at once because self-attention does not require each position to wait for the preceding position’s hidden state. An RNN’s state dependency makes position-wise computation sequential within an example. This is a difference in the structure of the computation, not a guarantee that every Transformer training run will be faster: implementation, hardware, model size, and workload matter.
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Parallel training should also be distinguished from autoregressive generation. A causal Transformer that predicts one token at a time still generates output step by step; caching and the amount of context retained affect the practical memory and latency costs.
Which is better for sequence tasks?
The answer depends on what the task demands. Use the comparison below as a starting point, then measure the actual model implementations you could deploy.
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| Decision factor | Self-attention / Transformer-style models | Conventional recurrent models |
|---|---|---|
| Training across positions | Positions can be processed concurrently during training, subject to the architecture and implementation. | Each position depends on the preceding recurrent state, so computation within an example proceeds sequentially. |
| Communication across distant positions | Attention can relate positions directly. | Information passes through successive state transitions; retaining long-range information depends on the recurrent design and learned state. |
| Long-sequence computation | Standard dense attention has quadratic sequence-length scaling; efficient-attention methods change this trade-off. | Processing remains stepwise; per-step computation and state requirements vary by architecture. |
| Streaming or incremental input | Causal variants can process or generate incrementally, but cache and memory requirements matter. | Designed to update state step by step; real latency and accuracy depend on the chosen model and implementation. |
| Best way to choose | Measure task quality, throughput, memory, latency, and deployment fit on the target workload. | Use the same data and measurements rather than assuming the architecture label predicts suitability. |
When self-attention may fit better
- Training throughput matters, and the workload can benefit from parallel computation over positions.
- The task depends on relationships between distant parts of an input.
- Your sequence lengths and available memory make the attention implementation practical.
When recurrence may fit better
- The system consumes a stream one item at a time and a recurrent state fits the deployment design.
- Stepwise computation is acceptable, or parallelism across positions is not the main constraint.
- A tested recurrent model meets the task’s quality, latency, and resource targets better than the alternatives.
These are selection considerations, not universal performance claims. A recurrent model is not automatically faster for streaming, and an attention model is not automatically more accurate for long-range tasks; benchmark the concrete implementations.
Does self-attention scale to long sequences?
Standard dense self-attention can become expensive as sequence length increases because its attention calculation scales quadratically with sequence length. That can make memory and computation limiting for long inputs. Whether the cost is acceptable depends on sequence size, implementation, hardware, and the rest of the model.
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Efficient-attention approaches seek to change that trade-off. For example, a 2020 paper proposes a kernel-feature formulation with linear sequence-length complexity under its method and assumptions (Katharopoulos et al., “Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention”). Linear scaling does not establish that every such method preserves the same quality or outperforms every RNN; evaluate the specific method on the target task.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the original Transformer results do—and do not—show
In its 2017 machine-translation experiments, the Transformer paper reported 28.4 BLEU on WMT 2014 English-to-German and 41.8 BLEU on WMT 2014 English-to-French. These are results from those experiments, not a controlled ranking of attention and recurrence across sequence tasks, later systems, or different evaluation setups. The results show what that paper achieved on its stated translation benchmarks; they cannot settle which architecture is best for your workload.
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How to compare models for your workload
- Fix the task and evaluation. Use the same training, validation, and test data, preprocessing, and quality metric for each candidate.
- Match sequence conditions. Include the input and output lengths the system will actually encounter, especially long-tail cases if they matter.
- Measure resource use. Record training throughput, peak memory, and inference latency under the hardware and batch conditions you expect to use.
- Test deployment behavior. For streaming or autoregressive use, measure incremental latency and memory as the context grows; do not infer them from training parallelism.
- Compare practical constraints. Account for implementation maturity and any limits on compute, memory, or serving. Choose the model that meets the required quality and operational targets.
The architecture space is not strictly binary. Universal Transformers, for example, combine self-attention with recurrent computation (Dehghani et al., “Universal Transformers”). Hybrid designs can be worth considering when a pure recurrent or attention-based model does not match the workload’s needs.
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