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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.
Agents excel at selecting knowledge sources but struggle with actual task completion, achieving only 56.1-75.3% accuracy in answers despite near-perfect routing.
LLMs can now be benchmarked for their ability to prepare training data, revealing that a new evaluation metric outperforms traditional methods in predicting downstream utility.