arXiv:2605.01188v2 Announce Type: replace Abstract: Scaling laws enable the optimal selection of data amount and language model size, yet the impact of the data unit, the token, on this relationship remains underexplored. In this work, we systematically investigate how the information granularity of tokens, controlled by the compression rate (i.e., average bytes of text per token), affects scaling trends. We train 988 latent tokenized models (BLT) ranging from 50M to 7B parameters that enable setting the desired compression rate. This flexibility allows us to study the role of compression rate
The proliferation of large language models and increasing computational demands necessitate a deeper understanding of underlying efficiency factors like tokenization.
Optimizing tokenization can significantly improve the computational efficiency, performance, and scaling laws of AI models, impacting the entire AI development landscape.
The explicit control and study of compression rate in tokenization provides a new lever for optimizing AI model training and deployment for specific tasks and resource constraints.
- · AI developers
- · Cloud computing providers
- · Researchers in AI efficiency
- · Hardware manufacturers for AI
- · Less efficient AI models
- · Organizations with high compute costs
More efficient AI models that can be trained and deployed with fewer resources.
Democratization of advanced AI capabilities due to reduced compute barriers.
Acceleration of AI research and development across various applications, potentially leading to new breakthroughs.
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