Scaling of WhiteMatter quality and systems trade-offs

Determine how WhiteMatter’s language-modeling quality and systems trade-offs scale with model size and data, including through evaluations of larger models and optimized end-to-end decoding.

Background

The reported experiments use relatively small models and an 8-billion-token training budget. Consequently, the paper does not establish whether the observed quality improvements, KV-cache benefits, and computational costs persist or change as model size and training data increase. The authors also identify the absence of an optimized end-to-end decoding benchmark, making larger-scale systems behavior unresolved.

References

These experiments therefore do not establish how the quality or systems trade-offs scale with model size and data. We report cache size and schedule convergence, but do not provide an optimized end-to-end decoding benchmark. Evaluating larger models and optimized end-to-end decoding remains future work.

— WhiteMatter: All-to-All Cross-Layer Connections via KV Mixing  (2608.18486 - Zhang et al., 19 Aug 2026) in Section “Limitations,” paragraph “Empirical scope”