Nexus: Same Pretraining Loss, Better Downstream Generalization via Common Minima
The foundational capabilities of large language models are acquired during pretraining on internet-scale, highly heterogeneous data mixtures. In this work, we investigate an interesting geometric question regarding the converged state of pretraining: Does the model converge to a common minimizer across all data sources (e.g., \cref{fig:cwa_illustration:close}), or merely a minimizer of the summed loss (e.g., \cref{fig:cwa_illustration:distant})? We hypothesize that the geometric "closeness" of task-specific minima is intrinsically linked to downstream generalization. We reveal that standard optimizers (e.g., AdamW) often converge to points where task-specific minima are distant from each ...
| Authors | Huanran Chen, Huaqing Zhang, Xiao Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 April 2026 |
| Last updated at source | 27 May 2026 |
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