Compute Optimal Tokenization: Scaling Laws for Data Compression in LLMs (co-tok.github.io)

Recent research titled "Compute Optimal Tokenization" investigates how data compression affects scaling laws in language models (LLMs). The key findings reveal that in compute-optimal scenarios, data measured in bytes should increase in tandem with the number of parameters, rather than simply scaling tokens. Moreover, there exists an optimal compression rate for training models that decreases as compute budgets increase. This underscores that as AI practitioners scale their models, understanding the relationship between data compression and model efficiency is crucial—especially since the optimal compression rate varies significantly between languages. The study also emphasizes that traditional approaches often fix the tokenizer while only varying model size and data amount. By allowing flexibility in the tokenizer's compression rate, the authors provide a roadmap for achieving better model performance across languages. With the implications of these findings, models trained in languages that require more data to convey the same meaning—highlighted through analysis like the IsoFLOP—could achieve improved results when accommodating language-specific compression rates. This research not only enriches the understanding of tokenization in model training but also suggests tailored strategies for optimizing language models across diverse linguistic contexts.