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The value of curated, capability-balanced supervision for adapting general language models to educational tasks spanning problem solving, curriculum understanding, and instructional support is demonstrated.
COAL-SQL is proposed, a unified framework combining Coverage-Guided Augmentation (CGA) and Failure-Driven Learning (FDL) that achieves 64.9% execution accuracy on the BIRD development set and outperforms baselines trained at comparable scale.
DataFlex-RL, an evaluation platform for comparing choices under a common GRPO recipe, is introduced, finding that changing the data policy measurably changes the training process but does not produce a reproducible improvement over uniform training.
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