What properties of reasoning supervision are associated with improved downstream model quality?

• URL: http://arxiv.org/abs/2605.13290v1
• Date: Wed, 13 May 2026 10:04:38 GMT
• Title: What properties of reasoning supervision are associated with improved downstream model quality?
• Abstract summary: 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.<n>Our analysis reveals that these intrinsic metrics demonstrate strong and significant correlations with downstream model performance.
• Score: 3.5354214473997225
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 precision, whereas larger models benefit from high redundancy, utilizing verbose traces to solve complex tasks. These findings establish a scale-aware framework for validating reasoning data, enabling practitioners to select effective training sets without the need for exhaustive empirical testing.

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