Generality of contribution dynamics beyond Pythia-family settings

Establish whether the stage-dependent training-data contribution dynamics identified for the Pythia and PolyPythia model configurations generalize to substantially different model architectures, datasets, and pretraining recipes.

Background

The study evaluates its task-agnostic contribution measure only on closely related Pythia and PolyPythia model families trained on related pretraining data. Although the observed contribution dynamics are broadly consistent across the configurations examined, the authors explicitly leave unresolved whether these patterns persist under substantially different architectures, datasets, and training procedures. Establishing this generality is important for determining whether the reported temporal shifts in influential training data reflect broader properties of language-model pretraining or are specific to the evaluated model families and experimental settings.