---
title: 'Fin-R1-Data: Financial Reasoning Dataset'
url: https://www.emergentmind.com/topics/fin-r1-data
type: topic
---
# Fin-R1-Data: Financial Reasoning Dataset
Fin-R1-Data refers to the specialized chain-of-thought (CoT) financial reasoning dataset constructed for the training and empirical validation of the Fin-R1 large language model, as detailed in "Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning" [2503.16252]. This dataset was designed to enhance the capability of language models to perform interpretable reasoning and robust decision-making across diverse financial tasks by distilling, structuring, and rigorously validating representative samples from heterogeneous financial sources. Below, the principal aspects and structural features of Fin-R1-Data are discussed in detail.
## 1. Construction and Structure of Fin-R1-Data
Fin-R1-Data is generated through a two-stage workflow. The initial stage involves the distillation and meticulous filtering of data from multiple raw financial sources, employing DeepSeek-R1 as the core extraction engine and an LLM-as-Judge for exhaustive data quality checks. Sources include financial question answering benchmarks and policy, regulatory, and quantitative datasets. The dataset systematically represents both reasoning and non-reasoning financial scenarios, ensuring diversity and coverage.
Every sample in Fin-R1-Data is structured as a triplet $(x, c, y^*)$:
- $x$: The financial question, potentially representing real-world exam, policy, or quantitative investment prompts.
- $c$: The chain-of-thought (CoT) reasoning, encapsulated within custom XML-tags …, documenting the stepwise deduction process.
- $y^*$: The definitive answer, enclosed in … tags, for clear extraction and comparison during training and evaluation.
This representation supports not only the learning of the reasoning process but also strict output validation and interpretability.
## 2. Data Sources and Quality Control
Raw inputs for Fin-R1-Data are derived from publicly available and proprietary financial datasets, with a specific focus on benchmarks such as FinQA and ConvFinQA, as well as datasets like FinPEE containing examination-style problems. Extensive filtering is employed using LLM-as-Judge, which assesses and ensures the presence of explicit, logically sound intermediate reasoning steps and answer correctness.
The quality filtering pipeline involves:
- Automated reasoning step extraction via DeepSeek-R1 distillation.
- Output format validation (tags for reasoning and final answers must be present).
- Manual and automated checks for content accuracy, logical flow, and non-redundant, representative reasoning traces.
Samples failing any stage are excluded, preventing logic gaps and formatting anomalies that could impair model training.
## 3. Formatting and Explicit Tagging
A salient feature of Fin-R1-Data is its explicit tagging scheme. All reasoning steps must be enclosed in …, and answers in …. The strict enforcement of this format serves dual purposes:
- Facilitates the extraction of reasoning and answer segments during training, enabling format reward integration in reinforcement learning.
- Ensures output interpretability at inference, allowing practitioners and auditors to directly inspect the model’s reasoning and answer generation.
This requirement is systematically enforced during both the supervised fine-tuning (SFT) and the reinforcement learning (RL) phases.
## 4. Training Regimen and Integration with Model Development
Fin-R1-Data is employed in a two-stage training methodology:
- **Supervised Fine-Tuning (SFT):** Each triplet trains the base model to produce logical and interpretable reasoning steps via explicit format learning.
- **Reinforcement Learning (RL):** Samples are used in format-and-accuracy reward design. The “accuracy reward” evaluates whether … matches reference answers; the “format reward” checks for compliance with the rigorous output template.
The reinforcement learning policy utilizes Group Relative Policy Optimization (GRPO), for which sample advantage $A_i$ is computed as:
$$
A_i = \frac{r_i - \mu}{\sigma}
$$
where $r_i$ is the sample reward and $\mu$, $\sigma$ denote the empirical mean and standard deviation over the group.
## 5. Coverage and Diversity Across Financial Tasks
Fin-R1-Data encompasses a broad range of financial reasoning scenarios:
- Arithmetic calculations, multi-step accounting operations, and quantitative problem solving.
- Regulatory compliance and policy-based reasoning tasks.
- Sentiment detection, classification of financial statements, and scenario-based analysis.
- Quantitative strategy code generation.
Coverage incorporates both simple and complex reasoning, with the explicit chain-of-thought traces allowing the model to generalize across tasks not explicitly seen in training.
## 6. Benchmark Representation and SOTA Evaluation Integration
Samples from FinQA and ConvFinQA appear within Fin-R1-Data, enabling direct benchmarking against mainstream financial reasoning tasks. The comprehensive structuring and explicit CoT make Fin-R1-Data uniquely suited for producing interpretable and explainable outputs, instrumental to the model achieving state-of-the-art results (FinQA: 76.0; ConvFinQA: 85.0) with only 7B parameters—surpassing larger models both in reasoning fidelity and overall accuracy.
## 7. Access and Implications for Research
Fin-R1-Data is made available as part of the open-source Fin-R1 release (https://github.com/SUFE-AIFLM-Lab/Fin-R1), providing researchers and developers access for further investigation, adaptation, and extension. Its structured design supports future work in automated financial reasoning, interpretable regulatory analysis, and domain-specific decision process auditing—establishing a standard for high-quality, chain-of-thought reasoning datasets in finance.
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Fin-R1-Data represents a rigorously constructed, explicitly formatted financial reasoning dataset. Its chain-of-thought traces and strict answer validation underpin state-of-the-art financial LLM training, enabling transparent, interpretable performance across complex financial tasks.
Source: https://www.emergentmind.com/topics/fin-r1-data