What properties of reasoning supervision are associated with improved downstream model quality?
Validating training data for reasoning models typically requires expensive trial-and-error fine-tuning cycles. In this work, we investigate whether the utility of a reasoning dataset can be reliably predicted prior to training using intrinsic data metrics. We propose a suite of quantitative measures and evaluate their predictive power by fine-tuning 8B and 11B models on semantically distinct variants of a Polish reasoning dataset. Our analysis reveals that these intrinsic metrics demonstrate strong and significant correlations with downstream model performance. Crucially, we find that the predictors of utility are scale-dependent: smaller models rely on alignment-focused metrics to ensure...
| Authors | Mikołaj Langner, Dzmitry Pihulski, Jan Eliasz et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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