Bottom line: Different layers perform different roles and could therefore enable non-uniform distribution of parameters and computational resources as an alternative to constant architectural width.
Researchers are exploring how Transformer models could use their parameters more efficiently by making the width of network layers variable rather than distributing them uniformly.
Previous scaling approaches for Transformer-based language models have primarily focused on increasing the depth and width of the network. However, the majority of established architectures maintain constant width across all layers and distribute parameters and computational budget uniformly—despite the fact that different layers potentially perform different computational tasks.
In an empirical investigation, the authors explore non-uniform capacity distribution across network depth. This involves testing a times-shaped pattern for modulating layer widths, in which network width varies at different positions in the model.
The approach makes it possible to remain within a fixed total computational budget while strategically concentrating parameters where they empirically deliver the highest value. This could be relevant for CTOs in optimizing training costs and inference latency as well as in calibrating hardware resource allocation.
Source: arxiv.org · Published June 15, 2026
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