Agentar-Fin-R1: Enhancing Financial Intelligence through Domain
  Expertise, Training Efficiency, and Advanced Reasoning
Large Language Models (LLMs) exhibit considerable promise in financial
applications; however, prevailing models frequently demonstrate limitations
when confronted with scenarios that necessitate sophisticated reasoning
capabilities, stringent trustworthiness criteria, and efficient adaptation to
domain-specific requirements. We introduce the Agentar-Fin-R1 series of
financial large language models (8B and 32B parameters), specifically
engineered based on the Qwen3 foundation model to enhance reasoning
capabilities, reliability, and domain specialization for financial
applications. Our optimization approach integrates a high-quality, systematic
financial task label system with a comprehensive multi-layered trustworthiness
assurance framework. This framework encompasses high-quality trustworthy
knowledge engineering, multi-agent trustworthy data synthesis, and rigorous
data validation governance. Through label-guided automated difficulty-aware
optimization, tow-stage training pipeline, and dynamic attribution systems, we
achieve substantial improvements in training efficiency. Our models undergo
comprehensive evaluation on mainstream financial benchmarks including Fineva,
FinEval, and FinanceIQ, as well as general reasoning datasets such as MATH-500
and GPQA-diamond. To thoroughly assess real-world deployment capabilities, we
innovatively propose the Finova evaluation benchmark, which focuses on
agent-level financial reasoning and compliance verification. Experimental
results demonstrate that Agentar-Fin-R1 not only achieves state-of-the-art
performance on financial tasks but also exhibits exceptional general reasoning
capabilities, validating its effectiveness as a trustworthy solution for
high-stakes financial applications. The Finova bench is available at
https://github.com/antgroup/Finova.
688358306d99b948a364d1a2
Yanjun Zheng
688358306d99b948a364d1a3
Xiyang Du
688358306d99b948a364d1a4
Longfei Liao
688358306d99b948a364d1a5
Xiaoke Zhao
688358306d99b948a364d1a6
Zhaowen Zhou
688358306d99b948a364d1a7
Jingze Song
688358306d99b948a364d1a8
Bo Zhang
688358306d99b948a364d1a9
Jiawei Liu
688358306d99b948a364d1aa
Xiang Qi
688358306d99b948a364d1ab
Zhe Li
688358306d99b948a364d1ac
Zhiqiang Zhang
688358306d99b948a364d1ad
Wei Wang
688358306d99b948a364d1ae
Peng Zhang
Agentar-Fin-R1: Enhancing Financial Intelligence through Domain
  Expertise, Training Efficiency, and Advanced Reasoning
Large Language Models (LLMs) exhibit considerable promise in financial
applications; however, prevailing models frequently demonstrate limitations
when confronted with scenarios that necessitate sophisticated reasoning
capabilities, stringent trustworthiness criteria, and efficient adaptation to
domain-specific requirements. We introduce the Agentar-Fin-R1 series of
financial large language models (8B and 32B parameters), specifically
engineered based on the Qwen3 foundation model to enhance reasoning
capabilities, reliability, and domain specialization for financial
applications. Our optimization approach integrates a high-quality, systematic
financial task label system with a comprehensive multi-layered trustworthiness
assurance framework. This framework encompasses high-quality trustworthy
knowledge engineering, multi-agent trustworthy data synthesis, and rigorous
data validation governance. Through label-guided automated difficulty-aware
optimization, tow-stage training pipeline, and dynamic attribution systems, we
achieve substantial improvements in training efficiency. Our models undergo
comprehensive evaluation on mainstream financial benchmarks including Fineva,
FinEval, and FinanceIQ, as well as general reasoning datasets such as MATH-500
and GPQA-diamond. To thoroughly assess real-world deployment capabilities, we
innovatively propose the Finova evaluation benchmark, which focuses on
agent-level financial reasoning and compliance verification. Experimental
results demonstrate that Agentar-Fin-R1 not only achieves state-of-the-art
performance on financial tasks but also exhibits exceptional general reasoning
capabilities, validating its effectiveness as a trustworthy solution for
high-stakes financial applications. The Finova bench is available at
https://github.com/antgroup/Finova.
The Agentar-Fin-R1 series of financial large language models, built on Qwen3, enhances reasoning, reliability, and domain specialization through a trustworthiness framework and achieves state-of-the-art performance on financial and general reasoning benchmarks.
Large Language Models financial applications reasoning capabilities trustworthiness criteria domain-specific requirements Qwen3 financial task label system multi-layered trustworthiness assurance framework trustworthy knowledge engineering multi-agent trustworthy data synthesis data validation governance label-guided automated difficulty-aware optimization tow-stage training pipeline dynamic attribution systems Fineva FinEval FinanceIQ MATH-500 GPQA-diamond Finova evaluation benchmark agent-level financial reasoning compliance verification