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This work constructs DataPraxis-5K, a dataset of 5,000 verified trajectories spanning diverse domains and output formats, to train DataPraxis-27B and DataPraxis-35B-A3B, an analytics task synthesis framework based on verified workspace evolution.
Remarkably, despite operating entirely without pre-loaded metadata, the proposed TRUST-SQL framework consistently matches or surpasses strong baselines that rely on schema prefilling.
AOPD replaces ineffective negative reinforcement with localized divergence minimization in non-positive advantage regions while preserving positive reinforcement learning and maintains higher policy entropy during training and better capability retention during sequential tool-use adaptation.
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