Spokes: Optimizing for Diverse Pretraining Data Selection
Diversity plays a critical role in data selection, improving performance under fixed data budgets by reducing redundancy and repetition. However, optimizing for diversity is inherently challenging, as it is a set-level property that depends on interactions between data points rather than individual examples. As a result, existing approaches typically rely on proxies or approximations, which often fail to ensure sufficiently diverse subsets. In this work, we directly optimize diversity by introducing a probabilistic diversification framework based on the G-Vendi score, optimized via exponentiated gradient descent. Our method produces subsets that are substantially more diverse than those o...
| Authors | Clarence Lee, Yejin Choi, Luke Zettlemoyer et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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