# DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large Language Models

arXiv: https://arxiv.org/abs/2504.15716 · HTML: https://mlsanity.com/paper/2504.15716/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [LIMO: Less is More for Reasoning](https://mlsanity.com/paper/2502.03387/) (2025)
- [Evaluation of OpenAI o1: Opportunities and Challenges of AGI](https://mlsanity.com/paper/2409.18486/) (2024)
- [Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning](https://mlsanity.com/paper/2503.16252/) (2025)
- [Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance](https://mlsanity.com/paper/2502.08127/) (2025)

## Built on by (most cited first, 22 of 22)

- [BizFinBench: A Business-Driven Real-World Financial Benchmark for Evaluating LLMs](https://mlsanity.com/paper/2505.19457/) (2025)
- [Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning](https://mlsanity.com/paper/2508.10848/) (2025)
- [FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning](https://mlsanity.com/paper/2506.02515/) (2025)
- [AI Can Learn Scientific Taste](https://mlsanity.com/paper/2603.14473/) (2026)
- [AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play](https://mlsanity.com/paper/2509.24193/) (2025)
- [Fin-PRM: A Domain-Specialized Process Reward Model for Financial Reasoning in Large Language Models](https://mlsanity.com/paper/2508.15202/) (2025)
- [Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning](https://mlsanity.com/paper/2507.16802/) (2025)
- [FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning](https://mlsanity.com/paper/2506.16123/) (2025)
- [FIRE: A Comprehensive Benchmark for Financial Intelligence and Reasoning Evaluation](https://mlsanity.com/paper/2602.22273/) (2026)
- [Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training](https://mlsanity.com/paper/2603.07223/) (2026)
- [Reinforcement Learning Enhanced Multi-hop Reasoning for Temporal Knowledge Question Answering](https://mlsanity.com/paper/2601.01195/) (2026)
- [FEVO: Financial Knowledge Expansion and Reasoning Evolution for Large Language Models](https://mlsanity.com/paper/2507.06057/) (2025)
- [RAAR: Retrieval Augmented Agentic Reasoning for Cross-Domain Misinformation Detection](https://mlsanity.com/paper/2601.04853/) (2026)
- [Mitigating Hallucination in Financial Retrieval-Augmented Generation via Fine-Grained Knowledge Verification](https://mlsanity.com/paper/2602.05723/) (2026)
- [FinReasoning: A Hierarchical Benchmark for Reliable Financial Research Reporting](https://mlsanity.com/paper/2603.19254/) (2026)
- [Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs](https://mlsanity.com/paper/2605.21975/) (2026)
- [Finance-Grounded Optimization For Algorithmic Trading](https://mlsanity.com/paper/2509.04541/) (2025)
- [BizFinBench.v2: Towards Reliable LLMs in Finance via Real-User Data and Offline/Online Bilingual Evaluation](https://mlsanity.com/paper/2601.06401/) (2026)
- [Structure First, Reason Next: Enhancing a Large Language Model using Knowledge Graph for Numerical Reasoning in Financial Documents](https://mlsanity.com/paper/2601.07754/) (2026)
- [PubSwap: Public-Data Off-Policy Coordination for Federated RLVR](https://mlsanity.com/paper/2604.12160/) (2026)
- [From Numbers to Judgment: Specialist LLM Agents and Reinforcement Learning for European Listed Real Estate](https://mlsanity.com/paper/2608.11381/) (2026)
- [PIVOT: Perplexity-Informed KD-to-RL Transition Scheduling for Vertical-Domain Few-Shot Distillation](https://mlsanity.com/paper/2610.11167/) (2026)

## Abstract (arXiv)

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.