title: DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large
    Language Models
summary: 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.
paper_authors._id: 680dcc5d3478de07603a8036
paper_authors.name: Jie Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-28T07:39:02.713Z
paper_authors._id: 642656cbad1e3b0e6e91b752
paper_authors.avatarUrl: /avatars/3bf0ee15fd528e09b2b889f5cce3cbd0.svg
paper_authors.fullname: Jie Zhu
paper_authors.type: user
paper_authors.user: amazingj
paper_authors._id: 680dcc5d3478de07603a8037
paper_authors.name: Qian Chen
paper_authors._id: 680dcc5d3478de07603a8038
paper_authors.name: Huaixia Dou
paper_authors._id: 680dcc5d3478de07603a8039
paper_authors.name: Junhui Li
paper_authors._id: 680dcc5d3478de07603a803a
paper_authors.name: Lifan Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:41:24.106Z
paper_authors._id: 62f0bb7f47cf7b3344fe9ad6
paper_authors.avatarUrl: /avatars/e509f31af551cfd30e319b03d4c92740.svg
paper_authors.fullname: guo
paper_authors.type: user
paper_authors.user: lifan
paper_authors._id: 680dcc5d3478de07603a803b
paper_authors.name: Feng Chen
paper_authors._id: 680dcc5d3478de07603a803c
paper_authors.name: Chi Zhang
paper_title: DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large
    Language Models
paper_summary: 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.
paper_ai_summary: A reasoning-enhanced framework, DianJin-R1, is proposed to improve financial domain reasoning in LLMs through augmented supervision and reinforcement learning, achieving superior performance on financial benchmarks.
paper_ai_keywords: reasoning-augmented supervision reinforcement learning DianJin-R1-Data Qwen2.5-7B-Instruct Qwen2.5-32B-Instruct Group Relative Policy Optimization GRPO structured outputs answer correctness CFLUE FinQA CCC MATH-500 GPQA-Diamond single-call reasoning multi-agent systems