Agentar-Fin-R1: Enhancing Financial Intelligence through Domain
  Expertise, Training Efficiency, and Advanced Reasoning
Authors: name: Yanjun Zheng, name: Xiyang Du, name: Longfei Liao, name: Xiaoke Zhao, name: Zhaowen Zhou, name: Jingze Song, name: Bo Zhang, name: Jiawei Liu, name: Xiang Qi, name: Zhe Li, name: Zhiqiang Zhang, name: Wei Wang, name: Peng Zhang

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.