Transformer vs RNN: Parallel Attention vs Sequential Recurrence

Mon, 03 Aug 2026 03:40:07 +0900

Overview

RNNs process sequences one token at a time, carrying context forward through a hidden state that updates at each step. Transformers instead process every token simultaneously, letting each position directly attend to every other via self-attention. The difference reshapes everything from training speed to how well long-range context survives.

Comparison Diagram

Comparison Table

Aspect	RNN	Transformer
Input processing order	Tokens consumed one at a time, in sequence	All tokens consumed simultaneously
Context propagation	Hidden state carried forward step to step	Self-attention lets each position read all others directly
Long-range dependencies	Signal weakens over distance (vanishing/exploding gradients)	Direct connection between any two positions regardless of distance
Positional information	Implicit, from the order tokens are fed in	Explicit, via added positional encodings
Training parallelization	Limited — must unroll and step through time	Fully parallel across the sequence dimension
Computational cost	O(n) sequential steps, O(1) state per step	O(n^2) attention cost over sequence length
Inference/generation	Constant memory, naturally one step at a time	Requires KV caching to avoid recomputing past attention
Typical use cases	LSTM/GRU for streaming or small-scale sequence tasks	BERT/GPT-style models for large-scale language and vision tasks

Key Differences

Transformer processes all tokens in parallel via self-attention; RNN processes tokens sequentially through a hidden state
RNN suffers from vanishing gradients over long sequences; Transformer links any two positions directly
Transformer needs explicit positional encodings since attention has no inherent order; RNN gets order for free
Transformer training scales with quadratic complexity in sequence length; RNN training is linear but hard to parallelize

When to Use Each

RNN

LSTM vs GRU: Three Gates vs Two Gates in Recurrent Memory

Mon, 03 Aug 2026 03:33:36 +0900

Overview

LSTM and GRU are both gated recurrent architectures built to capture long-range dependencies in sequences while avoiding the vanishing-gradient problem of vanilla RNNs. LSTM keeps a dedicated cell state alongside its hidden state, regulated by three gates, while GRU folds everything into a single hidden state updated by just two gates. That structural difference drives everything else: parameter count, training speed, and how precisely you can control what the network remembers.

Sequence-Modeling on IT Comparison

Transformer vs RNN: Parallel Attention vs Sequential Recurrence

Overview

Comparison Diagram

Comparison Table

Key Differences

When to Use Each

LSTM vs GRU: Three Gates vs Two Gates in Recurrent Memory

Overview