Researchers explain "hybrid width" nested submodel design: more freedom, harder config choice
nthngdy · x · 2026-08-20
The author explains why their new work uses a "hybrid width" setup: compared to taking subsets of a fixed-width model, it offers more configuration choices for the same target model sizes—more degrees of freedom but more headaches picking a config. Putting most of the max width in the smallest submodel and growing depth incrementally would yield a super deep model, unfair for speed/memory comparisons. The questioner argues plain subset selection (uniform width, training a subset of layers) should suffice and asks why per-layer widths are needed.
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