Why Transformers Outperform RNNs: A Deep Dive into Architecture and Training

What is a Transformer?

A Transformer processes a sequence of tokens (e.g., an English sentence) and produces another sequence (e.g., its Spanish translation) using only attention mechanisms. The architecture was introduced in the paper Attention Is All You Need (Vaswani et al., 2017) and underlies models such as BERT (encoder‑only) and the GPT series (decoder‑only).

Core Architecture

Both the encoder and the decoder consist of a stack of identical layers. Each encoder layer contains:

Multi‑head self‑attention sub‑layer.

Position‑wise feed‑forward network.

Residual connection and two LayerNorm operations surrounding the sub‑layers.

The decoder layer adds a second attention sub‑layer that attends to the encoder’s output (encoder‑decoder attention) before the feed‑forward block. All layers share the same internal structure, so a stack of N layers forms the encoder group and another stack forms the decoder group. The final linear projection maps decoder hidden states to a vocabulary distribution.

Self‑Attention Mechanism

For each token, self‑attention computes a weighted sum of all token representations, allowing the model to capture dependencies regardless of distance. Example:

Sentence: "The cat drank the milk because it was hungry." – the pronoun it receives high attention scores from cat and hungry , enabling correct coreference.

Sentence: "The cat drank the milk because it was sweet." – the same token it now attends to milk and sweet , showing context‑dependent weighting.

Multi‑head attention provides several independent sets of attention scores, so different heads can focus on different aspects (e.g., syntax vs. semantics).

Training Pipeline

Convert the source sequence (e.g., English) into token embeddings, add sinusoidal positional encodings, and feed the result to the encoder.

The encoder stack produces contextual representations for every source token.

Prepend a start‑of‑sentence token ( <SOS>) to the target sequence (e.g., Spanish), embed it, and feed it to the decoder.

The decoder attends simultaneously to its own previous embeddings (self‑attention) and to the encoder output (encoder‑decoder attention), yielding a representation for each target position.

A linear output layer projects each decoder representation to a probability distribution over the target vocabulary.

Cross‑entropy loss compares the predicted distribution with the ground‑truth target tokens; gradients are back‑propagated through the entire network.

During training the correct target token is supplied at each step – a technique called teacher forcing . This prevents error accumulation, enables parallel computation of all target positions, and dramatically speeds up training compared with a naïve auto‑regressive loop.

Inference (Decoding)

At inference time only the source sequence is available. Decoding proceeds iteratively until an <EOS> token is generated:

Embed the source and run it through the encoder once (the encoder output is cached – noted by Michal Kučírka).

Initialize the decoder input with <SOS>, embed it, and run it together with the cached encoder output. Project the decoder output to word probabilities and select the most likely token (greedy or beam search).

Append the selected token to the decoder input and repeat steps 2‑4.

Why Transformers Beat RNNs and CNNs

RNN‑based seq2seq models process tokens sequentially, limiting parallelism and making long‑range dependencies hard to learn. CNNs allow parallelism but restrict interaction to the kernel size, requiring many layers for distant tokens. Transformers eliminate recurrence entirely, enabling:

Full parallel processing of all tokens in both encoder and decoder.

Direct modeling of any pairwise token relationship, regardless of distance.

Significant speedup: the encoder is computed once, and the decoder can attend to the entire previously generated sequence at each step.

Typical Applications

Transformers serve as the backbone for a wide range of NLP tasks. After the shared encoder/decoder stack, a task‑specific “head” is attached:

Classification head – a simple feed‑forward layer that maps the pooled Transformer output to class logits (e.g., sentiment analysis, intent detection).

Language‑model head – a linear projection that produces a probability distribution over the vocabulary for next‑token prediction (used in GPT‑style models).

Sequence‑to‑sequence tasks such as machine translation, summarization, and question answering use the full encoder‑decoder pipeline.

Model Variants

Depending on the downstream task, different subsets of the architecture are used:

Encoder‑only (e.g., BERT, RoBERTa) – stack only the encoder; a classification or token‑level head is added on top.

Decoder‑only (e.g., GPT‑2, GPT‑3) – stack only the decoder; the language‑model head generates text autoregressively.

Encoder‑decoder (e.g., original Transformer, T5) – both stacks are present for full seq2seq modeling.

Key Advantages Summarized

Parallel computation of all positions reduces training time dramatically.

Attention scores are independent of token distance, eliminating the long‑range dependency problem of RNNs.

Multi‑head design allows the model to capture diverse linguistic phenomena simultaneously.

Understanding these mechanisms is essential for working with modern large‑scale language models, which are built on the Transformer foundation.