Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning

• URL: http://arxiv.org/abs/2507.16802v4
• Date: Sun, 27 Jul 2025 13:53:23 GMT
• Title: Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning
• Authors: 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,
• Abstract summary: We introduce the Agentar-Fin-R1 series of financial large language models.<n>Our optimization approach integrates a high-quality, systematic financial task label system.<n>Our models undergo comprehensive evaluation on mainstream financial benchmarks.
• Score: 12.548390779247987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) exhibit considerable promise in financial applications; however, prevailing models frequently demonstrate limitations when confronted with scenarios that necessitate sophisticated reasoning capabilities, stringent trustworthiness criteria, and efficient adaptation to domain-specific requirements. We introduce the Agentar-Fin-R1 series of financial large language models (8B and 32B parameters), specifically engineered based on the Qwen3 foundation model to enhance reasoning capabilities, reliability, and domain specialization for financial applications. Our optimization approach integrates a high-quality, systematic financial task label system with a comprehensive multi-layered trustworthiness assurance framework. This framework encompasses high-quality trustworthy knowledge engineering, multi-agent trustworthy data synthesis, and rigorous data validation governance. Through label-guided automated difficulty-aware optimization, tow-stage training pipeline, and dynamic attribution systems, we achieve substantial improvements in training efficiency. Our models undergo comprehensive evaluation on mainstream financial benchmarks including Fineva, FinEval, and FinanceIQ, as well as general reasoning datasets such as MATH-500 and GPQA-diamond. To thoroughly assess real-world deployment capabilities, we innovatively propose the Finova evaluation benchmark, which focuses on agent-level financial reasoning and compliance verification. Experimental results demonstrate that Agentar-Fin-R1 not only achieves state-of-the-art performance on financial tasks but also exhibits exceptional general reasoning capabilities, validating its effectiveness as a trustworthy solution for high-stakes financial applications. The Finova bench is available at https://github.com/antgroup/Finova.

Related papers

• FinS-Pilot: A Benchmark for Online Financial System [17.65500174763836]
  FinS-Pilot is a novel benchmark for evaluating large language models (RAGs) in online financial applications.<n>Our benchmark incorporates both real-time API data and structured text sources, organized through an intent classification framework.<n>Our work contributes both a practical evaluation framework and a curated dataset to advance research in financial NLP systems.
  arXiv  Detail & Related papers  (2025-05-31T03:50:19Z)
• FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs [15.230256296815565]
  FinMaster is a benchmark designed to assess the capabilities of large language models (LLMs) in financial literacy, accounting, auditing, and consulting.<n>FinMaster comprises three main modules: FinSim, FinSuite, and FinEval.<n>Experiments reveal critical capability gaps in financial reasoning, with accuracy dropping from over 90% on basic tasks to merely 37% on complex scenarios.
  arXiv  Detail & Related papers  (2025-05-18T11:47:55Z)
• FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation [63.55583665003167]
  We present FinDER, an expert-generated dataset tailored for Retrieval-Augmented Generation (RAG) in finance.<n>FinDER focuses on annotating search-relevant evidence by domain experts, offering 5,703 query-evidence-answer triplets.<n>By challenging models to retrieve relevant information from large corpora, FinDER offers a more realistic benchmark for evaluating RAG systems.
  arXiv  Detail & Related papers  (2025-04-22T11:30:13Z)
• DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large Language Models [13.567516575993546]
  We propose DianJin-R1, a reasoning-enhanced framework for large language models (LLMs) in the financial domain.<n>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)<n>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.
  arXiv  Detail & Related papers  (2025-04-22T09:01:04Z)
• KFinEval-Pilot: A Comprehensive Benchmark Suite for Korean Financial Language Understanding [6.3604109210772934]
  KFinEval-Pilot is a benchmark suite specifically designed to evaluate large language models (LLMs) in the Korean financial domain.<n>It comprises over 1,000 curated questions across three critical areas: financial knowledge, legal reasoning, and financial toxicity.
  arXiv  Detail & Related papers  (2025-04-17T00:12:58Z)
• FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting [58.70072722290475]
  Financial time series (FinTS) record the behavior of human-brain-augmented decision-making.<n>FinTSB is a comprehensive and practical benchmark for financial time series forecasting.
  arXiv  Detail & Related papers  (2025-02-26T05:19:16Z)
• Demystifying Domain-adaptive Post-training for Financial LLMs [79.581577578952]
  FINDAP is a systematic and fine-grained investigation into domain adaptive post-training of large language models (LLMs)<n>Our approach consists of four key components: FinCap, FinRec, FinTrain and FinEval.<n>The resulting model, Llama-Fin, achieves state-of-the-art performance across a wide range of financial tasks.
  arXiv  Detail & Related papers  (2025-01-09T04:26:15Z)
• Financial Knowledge Large Language Model [4.599537455808687]
  We introduce IDEA-FinBench, an evaluation benchmark for assessing financial knowledge in large language models (LLMs) We propose IDEA-FinKER, a framework designed to facilitate the rapid adaptation of general LLMs to the financial domain. Finally, we present IDEA-FinQA, a financial question-answering system powered by LLMs.
  arXiv  Detail & Related papers  (2024-06-29T08:26:49Z)
• SuperCLUE-Fin: Graded Fine-Grained Analysis of Chinese LLMs on Diverse Financial Tasks and Applications [17.34850312139675]
  SC-Fin is a pioneering evaluation framework tailored for Chinese-native financial large language models (FLMs) It assesses FLMs across six financial application domains and twenty-five specialized tasks. Using multi-turn, open-ended conversations that mimic real-life scenarios, SC-Fin measures models on a range of criteria.
  arXiv  Detail & Related papers  (2024-04-29T19:04:35Z)
• FinBen: A Holistic Financial Benchmark for Large Language Models [75.09474986283394]
  FinBen is the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks. FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading.
  arXiv  Detail & Related papers  (2024-02-20T02:16:16Z)
• FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets [9.714447724811842]
  This paper introduces a distinctive approach anchored in the Instruction Tuning paradigm for open-source large language models. We capitalize on the interoperability of open-source models, ensuring a seamless and transparent integration. The paper presents a benchmarking scheme designed for end-to-end training and testing, employing a cost-effective progression.
  arXiv  Detail & Related papers  (2023-10-07T12:52:58Z)
• PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance [63.51545277822702]
  PIXIU is a comprehensive framework including the first financial large language model (LLMs) based on fine-tuning LLaMA with instruction data. We propose FinMA by fine-tuning LLaMA with the constructed dataset to be able to follow instructions for various financial tasks. We conduct a detailed analysis of FinMA and several existing LLMs, uncovering their strengths and weaknesses in handling critical financial tasks.
  arXiv  Detail & Related papers  (2023-06-08T14:20:29Z)
• FinQA: A Dataset of Numerical Reasoning over Financial Data [52.7249610894623]
  We focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. We propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge.
  arXiv  Detail & Related papers  (2021-09-01T00:08:14Z)
• Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning [94.65749466106664]
  A fundamental aspect of fairness that has not been explored in financial deep learning is the concept of trust. We conduct multi-scale trust quantification on a deep neural network for the purpose of credit card default prediction.
  arXiv  Detail & Related papers  (2020-11-03T19:05:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.