AI RESEARCH
GRACE: Gradient-aligned Reasoning Data Curation for Efficient Post-training
arXiv CS.AI
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ArXi:2605.13130v1 Announce Type: new 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.