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ARXIV:2605.13290 · REASONING DATA VALIDATION · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.13290REASONING DATA VALIDATIONSUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHMikołaj Langner · Dzmitry Pihulski · Jan Eliasz · Michał Rajkowski · Przemysław Kazienko · Maciej Piasecki · +2 at arXiv
A framework using intrinsic data metrics to predict the utility of reasoning datasets, enabling practitioners to select effective training sets without extensive fine-tuning.
Opportunity summary
Pain A framework using intrinsic data metrics to predict the utility of reasoning datasets, enabling practitioners to select effective training sets without extensive fine-tuning.
Evidence 0 refs | 0 sources | 0% coverage
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A framework using intrinsic data metrics to predict the utility of reasoning datasets, enabling practitioners to select effective training sets without extensive fine-tuning. In this work, we investigate whether the utility of a reasoning…
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…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our analysis reveals that these intrinsic metrics demonstrate strong and significant correlations with downstream model performance. Code availability is flagged in the production record;…
Reasoning Data Validation moved forward this cycle; last verified May 2026. Public score 5.0/10. Production flags indicate code availability.
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A framework using intrinsic data metrics to predict the utility of reasoning datasets, enabling practitioners to select effective training sets without extensive fine-tuning.
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10.48550/arXiv.2605.13290A framework using intrinsic data metrics to predict the utility of reasoning datasets, enabling practitioners to select effective training sets without extensive fine-tuning.
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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PROBLEM
A framework using intrinsic data metrics to predict the utility of reasoning datasets, enabling practitioners to select effective training sets without extensive fine-tuning. In this work, we investigate whether the utility of a reasoning dataset can be reliably predicted prior...
METHOD
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.
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our analysis reveals that these intrinsic metrics demonstrate strong and significant correlations with downstream model performance. Code availability is flagged in the production record; the public repos...
WHY NOW
Reasoning Data Validation moved forward this cycle; last verified May 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework using intrinsic data metrics to predict the utility of reasoning datasets, enabling practitioners to select effective training sets without extensive fine-tuning. In this work, we investigate whether the utility of a reasoning dataset can be reliably predicted prior to training using intrinsic data metrics.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our analysis reveals that these intrinsic metrics demonstrate strong and significant correlations with downstream model performance. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reasoning Data Validation moved forward this cycle; last verified May 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework using intrinsic data metrics to predict the utility of reasoning datasets, enabling practitioners to select effective training sets without extensive fine-tuning.
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