Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training

• URL: http://arxiv.org/abs/2603.07223v1
• Date: Sat, 07 Mar 2026 14:07:37 GMT
• Title: Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training
• Abstract summary: Large Language Models (LLMs) have demonstrated strong general capabilities, yet their deployment in finance remains challenging.<n>We introduce textbfODA-Fin-SFT-318k, constructed via multi-stage distillation and verification to produce high-quality Chain-of-Thought supervision.<n>We show that high-quality CoT distillation establishes a robust foundation during SFT, while difficulty- and verifiability-aware sampling improves RL generalization.
• Score: 37.29426753207275
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated strong general capabilities, yet their deployment in finance remains challenging due to dense domain-specific terminology, stringent numerical reasoning requirements, and low tolerance for factual errors. We conduct a controlled empirical study showing that in specialized vertical domains, performance is largely determined by the quality and difficulty/verifiability profile of post-training data. We introduce \textbf{ODA-Fin-SFT-318k}, constructed via multi-stage distillation and verification to produce high-quality Chain-of-Thought supervision, and \textbf{ODA-Fin-RL-12k}, curated for hard-but-verifiable tasks that balance reward precision and task diversity. Using standard SFT and RL pipelines, we show that high-quality CoT distillation establishes a robust foundation during SFT, while difficulty- and verifiability-aware sampling improves RL generalization. Evaluated on nine benchmarks spanning general financial tasks, sentiment analysis, and numerical reasoning, our ODA-Fin-RL-8B consistently surpasses open-source state-of-the-art (SOTA) financial LLMs of comparable size. We release our ODA-Fin-SFT-318k and ODA-Fin-RL-12k datasets, along with trained models to advance data-centric financial AI research.

Related papers

• Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability [129.1296673737603]
  Large language models (LLMs) have demonstrated strong reasoning capabilities through step-by-step chain-of-thought (CoT) reasoning.<n>A potential alternative is divide-and-conquer (DAC) reasoning, which decomposes a complex problem into subproblems to facilitate more effective exploration of the solution.<n>We propose an end-to-end reinforcement learning (RL) framework to enhance their DAC-style reasoning capacity.
  arXiv  Detail & Related papers  (2026-02-02T18:54:54Z)
• Financial Instruction Following Evaluation (FIFE) [4.4409035166872135]
  We introduce FIFE, a novel, high-difficulty benchmark designed to assess LM instruction-following capabilities for financial analysis tasks.<n> FIFE comprises 88 human-authored prompts and employs a verification system with chainable, verifiable constraints for fine-grained reward signals.<n>We release our dataset and code as an open-source resource to promote research in Reinforcement Learning for the financial domain.
  arXiv  Detail & Related papers  (2025-12-01T00:39:19Z)
• OpenMMReasoner: Pushing the Frontiers for Multimodal Reasoning with an Open and General Recipe [69.90298686714036]
  We introduce OpenMMReasoner, a fully transparent two-stage recipe for multimodal reasoning spanning fine-tuning and reinforcement learning.<n>Our method achieves a 11.6% improvement over the Qwen2.5-VL-7B-Instruct baseline across nine multimodal reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-11-20T13:11:45Z)
• FinSight: Towards Real-World Financial Deep Research [68.31086471310773]
  FinSight is a novel framework for producing high-quality, multimodal financial reports.<n>To ensure professional-grade visualization, we propose an Iterative Vision-Enhanced Mechanism.<n>A two-stage Writing Framework expands concise Chain-of-Analysis segments into coherent, citation-aware, and multimodal reports.
  arXiv  Detail & Related papers  (2025-10-19T14:05:35Z)
• Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning [12.548390779247987]
  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.
  arXiv  Detail & Related papers  (2025-07-22T17:52:16Z)
• FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning [82.7292329605713]
  FinChain is the first benchmark specifically designed for verifiable Chain-of-Thought evaluation in finance.<n>It spans 58 topics across 12 financial domains, each represented by parameterized symbolic templates with executable Python traces.<n>FinChain exposes persistent weaknesses in multi-step financial reasoning and provides a foundation for developing trustworthy, interpretable, and verifiable financial AI.
  arXiv  Detail & Related papers  (2025-06-03T06:44:42Z)
• 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)
• 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)
• Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance [35.617409883103335]
  FinReason is the first financial reasoning benchmark covering multi-table analysis, long-context reasoning, and equation-based tasks.<n>We introduce FinCoT, the first open high-fidelity CoT corpus for finance, distilled from seven QA datasets.<n>We develop Fin-o1, the first open financial reasoning models trained via supervised fine-tuning and GRPO-based RL.
  arXiv  Detail & Related papers  (2025-02-12T05:13:04Z)
• FinanceQA: A Benchmark for Evaluating Financial Analysis Capabilities of Large Language Models [0.0]
  FinanceQA is a testing suite that evaluates LLMs' performance on complex numerical financial analysis tasks that mirror real-world investment work.<n>Current LLMs fail to meet the strict accuracy requirements of financial institutions, with models failing approximately 60% of realistic tasks.<n>Results show that higher-quality training data is needed to support such tasks, which we experiment with using OpenAI's fine-tuning API.
  arXiv  Detail & Related papers  (2025-01-30T00:06:55Z)
• 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)

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.