Seq2Seq: Training and Decoding a Generative Pipeline
Seq2seq is the encoder-decoder pattern (Topic 116) industrialized: the 2014 deep-LSTM translation machine, teacher forcing for stable training, exposure bias as its shadow, and beam search as the inference contract. These mechanics still run every generation system — including 2026 LLM servers.
01.The Problem: How Do You Train Something That Writes Its Own Input?
Topic 116 left us with two networks: an encoder that compresses the source, a decoder that generates the target, conditioned on the memo c.
But the decoder faces a strange loop:
- To produce word 4, it must feed in word 3.
- During training, who supplies word 3 — the answer key, or the model's own half-baked guess?
- During deployment, the answer key does not exist. The model walks into the exam alone.
And there is a second question the whole field needed answered in 2014:
Can one neural system really translate French and English well enough to beat the statistical pipelines that had run Google Translate for a decade?
"Seq2Seq Learning with Neural Networks" (Sutskever et al., 2014) answered both — with a machine so influential that its training and decoding recipe is still the contract under ChatGPT-style servers. This topic is that recipe and its two shadows: exposure bias and the O(T) wall.
Two Regimes of the Same Network: Forcing vs Free-Run 🎬
Two Regimes of the Same Network: Forcing vs Free-Run 🎬
Training parallelizes credit assignment with real prefixes; generation must survive its own mistakes. Every seq2seq (and every LLM serving decision) is shaped by this split.
Unlock Topic #117: Seq2Seq: Training and Decoding a Generative Pipeline
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