arXiv AI
A Scaled-Up Empirical Study of Syntactic Language Models
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Synthetic Pre-pretraining Survives Scale, but Not as a Grammatical Prior
The study evaluates synthetic pre‑pretraining (PPT) across a wide range of models (500 M–7 B parameters) and training budgets (up to 100 B tokens). Results show that PPT consistently improves downstream performance and token efficiency, saving at least 21 B tokens at the 3 B scale, but these gains do not appear to stem from a grammatical prior. Instead, PPT benefits arise from tasks that enhance long‑range retrieval, and the improvements remain robust across diverse data mixtures, diminishing only when web text is omitted.
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Language Models Can Control Their Own Attention
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arXiv AI
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Structural Generalization on SLOG without Hand-Written Rules
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MS-GLA: Multi-Scale Gated Linear Attention for Addressing Representational Bottlenecks via Multi-Temporal Resolution
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arXiv Computation and Language
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No Single Tokenizer Feature Reliably Predicts Downstream Language Model Performance
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