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Alexander Fraser

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Preprint Aug 2026

Squeezing More from Limited Data with Recursive Transformers

Recursion Transformers are studied, reusing a shared block across depth to scale compute, together with factorized embeddings to reduce vocabulary-map parameters, and find that they outperform standard Transformers at 10M and 100M words, while remaining competitive with BabyLM Challenge 2025 winners.

Serdar Gülbahar, Lukas Edman, Alexander Fraser · 0 citations

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