arXiv:2605.26971v1 Announce Type: new Abstract: The proliferation of Reinforcement Learning from Verifiable Rewards (RLVR) datasets has exacerbated provenance collapse due to unclear lineage among existing datasets. To bridge this fragmented RLVR data landscape, we propose Atomic-source Tracing via Lineage-Aware Search (ATLAS), a systematic framework for tracing RLVR datasets back to their atomic sources, attributing over 99.7% of 1.45M instances to 20 atomic sources. Our analysis reveals that most RLVR datasets are variants of a small set of shared upstream sources, with few introducing genui
The rapid expansion of RLVR datasets has created a critical need for better data provenance, leading researchers to develop systems for tracing data lineage.
Understanding the true origins and variations of RLVR datasets is crucial for developing robust, unbiased, and generalizable AI models by preventing the perpetuation of flaws from upstream sources.
The ability to systematically trace RLVR datasets back to atomic sources improves transparency in data creation and reuse, enabling more informed choices in model training and dataset curation.
- · AI researchers
- · Dataset curators
- · AI model developers
- · Developers of low-quality or non-original datasets
Improved clarity regarding the originality and diversity of RLVR datasets will lead to more targeted dataset creation and refinement.
Better data lineage could foster greater data sharing and collaboration, as sources and contributions become transparent and attributable.
The application of similar lineage tracing frameworks may extend to other complex AI data types, promoting better overall data governance and quality across AI development.
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Read at arXiv cs.LG