Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning
Yanjun Zheng, Xiyang Du, Longfei Liao, Xiaoke Zhao, Zhaowen Zhou, Jingze Song, Bo Zhang, Jiawei Liu, Xiang Qi, Zhe Li, Zhiqiang Zhang, Wei Wang, Peng Zhang
2025-07-25
Summary
This paper talks about Agentar-Fin-R1, a series of financial large language models built on the Qwen3 platform that improve financial reasoning, reliability, and domain-specific knowledge using a special trustworthiness framework.
What's the problem?
Many existing AI models struggle to handle the complex reasoning and strict trust needs required in financial applications, which makes them less useful and less reliable for important financial decisions.
What's the solution?
The researchers built Agentar-Fin-R1 with advanced training techniques like automated difficulty-aware optimization and multi-layered trust checks. They created a detailed system to label financial tasks accurately, combined trustworthy data sources, and set up strong validation rules to train efficient, reliable models that excel at both financial and general reasoning.
Why it matters?
This matters because Agentar-Fin-R1 sets new standards for AI in finance by offering trustworthy, expert-level reasoning that helps with tasks like compliance and decision-making, making AI safer and more effective for high-stakes financial environments.
Abstract
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.