RegMix-D: Dynamic Data Mixing via Proxy Training Trajectories
Data mixture selection is critical for Large Language Model pretraining. Existing methods such as RegMix select a single static mixture by fitting a regression model on small-scale proxy runs. We propose RegMix-D, a simple extension of RegMix to dynamic mixing. Our key observation is that proxy runs produce not only endpoint losses, but also full loss trajectories, which can be used to further improve data mixture. By training regression model on these trajectories, we can predict optimal mixtures at multiple training stages. RegMix-D supports two deployment modes: an offline variant that generates a complete mixture schedule before target training, and an online variant that adapts the m...
| Authors | Kaiyan Zhao, Zhongtao Miao, Akiko Aizawa et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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