DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large
  Language Models
Authors: name: Jie Zhu, name: Qian Chen, name: Huaixia Dou, name: Junhui Li, name: Lifan Guo, name: Feng Chen, name: Chi Zhang
Organization: Qwen DianJin

Effective reasoning remains a core challenge for large language models (LLMs)
in the financial domain, where tasks often require domain-specific knowledge,
precise numerical calculations, and strict adherence to compliance rules. We
propose DianJin-R1, a reasoning-enhanced framework designed to address these
challenges through reasoning-augmented supervision and reinforcement learning.
Central to our approach is DianJin-R1-Data, a high-quality dataset constructed
from CFLUE, FinQA, and a proprietary compliance corpus (Chinese Compliance
Check, CCC), combining diverse financial reasoning scenarios with verified
annotations. Our models, DianJin-R1-7B and DianJin-R1-32B, are fine-tuned from
Qwen2.5-7B-Instruct and Qwen2.5-32B-Instruct using a structured format that
generates both reasoning steps and final answers. To further refine reasoning
quality, we apply Group Relative Policy Optimization (GRPO), a reinforcement
learning method that incorporates dual reward signals: one encouraging
structured outputs and another rewarding answer correctness. We evaluate our
models on five benchmarks: three financial datasets (CFLUE, FinQA, and CCC) and
two general reasoning benchmarks (MATH-500 and GPQA-Diamond). Experimental
results show that DianJin-R1 models consistently outperform their non-reasoning
counterparts, especially on complex financial tasks. Moreover, on the
real-world CCC dataset, our single-call reasoning models match or even surpass
the performance of multi-agent systems that require significantly more
computational cost. These findings demonstrate the effectiveness of DianJin-R1
in enhancing financial reasoning through structured supervision and
reward-aligned learning, offering a scalable and practical solution for
real-world applications.