GRACE: Gradient-aligned Reasoning Data Curation for Efficient Post-training

Computer Science > Artificial Intelligence [Submitted on 13 May 2026] GRACE: Gradient-aligned Reasoning Data Curation for Efficient Post-training Junjie Li, Ziao Wang, NingXuan Ma, Jianghong Ma, Xiaofeng Zhang Existing reasoning data curation pipelines score whole samples, treating every intermediate step as equally valuable. In reality, steps within a trace contribute very unevenly, and selecting reasoning data well requires assessing them individually. We present GRACE, a gradient-aligned curation method that views each reasoning trace as a sequence of optimization events and scores every step by two complementary signals: its alignment with the answer-oriented gradient direction, and its consistency with the preceding reasoning trajectory. Step-level scores are aggregated into a sample-level value for subset selection, using only the model's internal optimization signals and no external reward models or step annotations. To make this scalable, GRACE introduces a representation-level gradient proxy that estimates step-level alignment from token-level upstream signals in a single forward pass. Post-training Qwen3-VL-2B-Instruct on MMathCoT-1M, GRACE reaches 108.8% of the full-data performance with 20% of the data and retains 100.2% with only 5%, with subsets that transfer effectively across model backbones. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.13130 [cs.AI]   (or arXiv:2605.13130v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.13130 Focus to learn more Submission history From: Junjie Li [view email] [v1] Wed, 13 May 2026 07:55:39 UTC (708 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)