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The Sequence Knowledge #850: The Unexpected Comeback of RNNs

Transformers dominate sequence modeling but suffer from O(N²) KV cache costs for long contexts. New RNNs with larger states, data-dependent gating, and LLM-era training recipes match Transformer perplexity at scale while maintaining O(1) inference cost, heralding a renaissance.

SourceTheSequenceAuthor: Jesus Rodriguez
The Sequence Knowledge #850: The Unexpected Comeback of RNNs
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Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.

Key points and analysis

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Key points

  • Transformer's KV cache leads to quadratic memory and compute costs with sequence length.
  • New RNNs feature larger hidden states, data-dependent gating, and modern training techniques.
  • These RNNs match Transformer perplexity at scale with O(1) inference complexity.
  • The RNN comeback is not nostalgia but an architectural evolution.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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