Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training
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 ODA-Fin-SFT-318k, constructed via multi-stage distillation and verification to produce high-quality Chain-of-Thought supervision, and 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.
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Chuxue Cao
claimed_verified
2026-03-10T09:44:55.785Z
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Chuxue Cao
chuxuecao
user
chuxuecao
69af9505b3ab31dee1281d4d
Honglin Lin
claimed_verified
2026-04-01T08:36:48.731Z
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Honglin Lin
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user
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Zhanping Zhong
claimed_verified
2026-03-11T16:31:52.522Z
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Zhanping Zhong
ChampionZhong
user
ChampionZhong
69af9505b3ab31dee1281d4f
Xin Gao
69af9505b3ab31dee1281d50
Mengzhang Cai
69af9505b3ab31dee1281d51
Conghui He
69af9505b3ab31dee1281d52
Sirui Han
69af9505b3ab31dee1281d53
Lijun Wu
Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training
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 ODA-Fin-SFT-318k, constructed via multi-stage distillation and verification to produce high-quality Chain-of-Thought supervision, and 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.
High-quality dataset curation and specialized training methods improve financial language model performance through enhanced reasoning and reduced factual errors.
Chain-of-Thought SFT RL CoT distillation difficulty-aware sampling verifiability-aware sampling financial LLMs ODA-Fin-SFT-318k ODA-Fin-RL-12k ODA-Fin-RL-8B