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Data-Centric Post-Training for Financial Reasoning: Mining, Distillation, and Verifiable Learning

arXiv · AI, language, vision and robotics · article · Sep 9, 2026 · UTC

Financial text, textbooks, and question-answer pairs are abundant, but only a small fraction is directly usable for reasoning-focused post-training. Existing QA pairs often lack explicit reasoning, sufficient context, or reliably verifiable answers, while textbooks must first be transformed into synthetic training examples. We present a data-centric pipeline that constructs complementary corpora by mining open-source reasoning traces, distilling financial instruction data, and generating knowledge-graph-guided question-answer pairs from financial educational material. After semantic deduplicat

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