Closing the Data Loop: Using OpenDataArena to Engineer Superior Training Datasets

• URL: http://arxiv.org/abs/2601.09733v1
• Date: Tue, 30 Dec 2025 17:46:38 GMT
• Title: Closing the Data Loop: Using OpenDataArena to Engineer Superior Training Datasets
• Authors: Xin Gao, Xiaoyang Wang, Yun Zhu, Mengzhang Cai, Conghui He, Lijun Wu,
• Abstract summary: We propose a paradigm shift from ad-hoc curation to a closed-loop dataset engineering framework using OpenDataArena (ODA)<n>We instantiate this methodology through two new datasets: textbfODA-Math460-k, a specialized mathematics reasoning dataset that utilizes a novel two-stage difficulty-aware pipeline to achieve State-of-the-Art (SOTA) results on benchmarks such as AIME and HMMT, and textbfODA-Mixture (100k & 500k), a series of multi-domain instruction datasets built via an Anchor-and-
• Score: 46.480867560675584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The construction of Supervised Fine-Tuning (SFT) datasets is a critical yet under-theorized stage in the post-training of Large Language Models (LLMs), as prevalent practices often rely on heuristic aggregation without a systematic understanding of how individual samples contribute to model performance. In this report, we propose a paradigm shift from ad-hoc curation to a closed-loop dataset engineering framework using OpenDataArena (ODA), which leverages value-anchored rankings and multi-dimensional analysis to transform value benchmarking into feedback signals guiding dataset construction. We instantiate this methodology through two new datasets: \textbf{ODA-Math-460k}, a specialized mathematics reasoning dataset that utilizes a novel two-stage difficulty-aware pipeline to achieve State-of-the-Art (SOTA) results on benchmarks such as AIME and HMMT, and \textbf{ODA-Mixture (100k \& 500k)}, a series of multi-domain instruction datasets built via an ``Anchor-and-Patch'' strategy that outperforms significantly larger open-source baselines. Our empirical results demonstrate that ODA-driven datasets significantly improve both domain-specific reasoning and general utility while achieving superior data efficiency, validating a transition toward data-centric AI where transparent evaluation serves as the primary engine for engineering high-quality training data.

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