Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training
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| Format: | Preprint |
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2026
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| author | Cao, Chuxue Lin, Honglin Zhong, Zhanping Gao, Xin Cai, Mengzhang He, Conghui Han, Sirui Wu, Lijun |
| author_facet | Cao, Chuxue Lin, Honglin Zhong, Zhanping Gao, Xin Cai, Mengzhang He, Conghui Han, Sirui Wu, Lijun |
| contents | Large Language Models (LLMs) have demonstrated strong general capabilities, yet their deployment in finance remains challenging due to dense domain-specific terminology, stringent numerical reasoning requirements, and low tolerance for factual errors. We conduct a controlled empirical study showing that in specialized vertical domains, performance is largely determined by the quality and difficulty/verifiability profile of post-training data. We introduce \textbf{ODA-Fin-SFT-318k}, constructed via multi-stage distillation and verification to produce high-quality Chain-of-Thought supervision, and \textbf{ODA-Fin-RL-12k}, curated for hard-but-verifiable tasks that balance reward precision and task diversity. Using standard SFT and RL pipelines, we show that high-quality CoT distillation establishes a robust foundation during SFT, while difficulty- and verifiability-aware sampling improves RL generalization. Evaluated on nine benchmarks spanning general financial tasks, sentiment analysis, and numerical reasoning, our ODA-Fin-RL-8B consistently surpasses open-source state-of-the-art (SOTA) financial LLMs of comparable size. We release our ODA-Fin-SFT-318k and ODA-Fin-RL-12k datasets, along with trained models to advance data-centric financial AI research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07223 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training Cao, Chuxue Lin, Honglin Zhong, Zhanping Gao, Xin Cai, Mengzhang He, Conghui Han, Sirui Wu, Lijun Machine Learning Large Language Models (LLMs) have demonstrated strong general capabilities, yet their deployment in finance remains challenging due to dense domain-specific terminology, stringent numerical reasoning requirements, and low tolerance for factual errors. We conduct a controlled empirical study showing that in specialized vertical domains, performance is largely determined by the quality and difficulty/verifiability profile of post-training data. We introduce \textbf{ODA-Fin-SFT-318k}, constructed via multi-stage distillation and verification to produce high-quality Chain-of-Thought supervision, and \textbf{ODA-Fin-RL-12k}, curated for hard-but-verifiable tasks that balance reward precision and task diversity. Using standard SFT and RL pipelines, we show that high-quality CoT distillation establishes a robust foundation during SFT, while difficulty- and verifiability-aware sampling improves RL generalization. Evaluated on nine benchmarks spanning general financial tasks, sentiment analysis, and numerical reasoning, our ODA-Fin-RL-8B consistently surpasses open-source state-of-the-art (SOTA) financial LLMs of comparable size. We release our ODA-Fin-SFT-318k and ODA-Fin-RL-12k datasets, along with trained models to advance data-centric financial AI research. |
| title | Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.07223 |