Scaling behavior of token-level dynamic grouping to billion-parameter models

Determine how token-level dynamic grouping, implemented by defining patches via BPE token character sequences with explicit end-of-patch markers and a second-stage hierarchical BPE compression, scales when applied to large-scale language models with billions of parameters, specifically characterizing its effects on training dynamics, memory requirements, and optimization challenges.

Background

The paper proposes a dynamic character grouping method that uses existing BPE token boundaries to define patches, adds explicit end-of-patch markers, and applies a second-stage hierarchical BPE to control patch granularity. This approach aims to combine the flexibility of character-level modeling with the efficiency of subword tokenization, and is evaluated on medium-scale settings across language modeling and downstream tasks.

While demonstrating strong performance relative to baselines, the paper does not include evaluations on models with billions of parameters. The authors explicitly note that scaling to such large regimes may present different training dynamics, memory constraints, and optimization challenges, leaving the scalability of token-level dynamic grouping in these regimes unresolved.