Densing law for user representation learning: tokenizing behavior breaks billion-scale scaling bottleneck
_reachsumit · x · 2026-08-25
The paper proposes a User Behavioral Densing Law characterizing the quantitative relationship between data scale and minimum sufficient tokenization capacity. On billion-scale Alipay data, raw data scaling hits a bottleneck, while tokenization yields sustained gains. They find an approximately linear log-log relationship between minimum sufficient tokenization capacity and input data size, with slope varying by method and data source.
Related event: Ant Group proposes densification law for billion-scale user representation(2 posts)→
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