Intrinsic Data Metrics Predict Reasoning Model Quality
A new study on arXiv (2605.13290) investigates whether the utility of reasoning datasets can be predicted before training using intrinsic data metrics. Researchers fine-tuned 8B and 11B models on variants of a Polish reasoning dataset and found strong correlations between these metrics and downstream performance. The predictors are scale-dependent: smaller models need alignment-focused metrics for precision, while larger models benefit from high redundancy and verbose traces. This framework enables practitioners to select effective training sets without costly trial-and-error.
Key facts
- arXiv paper 2605.13290
- Announce Type: new
- Validating training data for reasoning models typically requires expensive trial-and-error fine-tuning cycles.
- Study investigates whether utility of reasoning dataset can be predicted prior to training using intrinsic data metrics.
- Proposes a suite of quantitative measures.
- Evaluated predictive power by fine-tuning 8B and 11B models on semantically distinct variants of a Polish reasoning dataset.
- Intrinsic metrics demonstrate strong and significant correlations with downstream model performance.
- Predictors of utility are scale-dependent: smaller models rely on alignment-focused metrics, larger models benefit from high redundancy.
Entities
Institutions
- arXiv