Create README.md

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: OpenDataArena/ODA-Fin-SFT-8B
+tags:
+- finance
+- reasoning
+- reinforcement-learning
+- GRPO
+model-index:
+- name: ODA-Fin-RL-8B
+  results: []
+datasets:
+- OpenDataArena/ODA-Fin-SFT-318k
+- OpenDataArena/ODA-Fin-RL-12k
+language:
+- en
+- zh
+metrics:
+- accuracy
+- f1
+size_categories:
+- 10K<n<100K
+---
+
+
+<div align="center">
+  <h1>Unlocking Data Value in Finance: A Study on Distillation
+and Difficulty-Aware Training</h1>
+
+</div>
+
+<div align="center">
+  
+[![Paper](https://img.shields.io/badge/arXiv-Paper-red)](https://arxiv.org/abs/2603.07223)
+[![Collections](https://img.shields.io/badge/🤗-Collections-yellow)](https://huggingface.co/collections/OpenDataArena/oda-finance)
+
+</div>
+
+<figure align="center">
+  <img src="imgs/model_compare.png" width="100%" alt="Model Performance Comparison">
+  <figcaption><em>Average score across Financial benchmarks. ODA-Fin-RL/SFT-8B demonstrates strong performance relative to thinking models with significantly more parameters.</em></figcaption>
+</figure>
+
+---
+
+This repository provides **ODA-Fin-RL-8B**, the reinforcement learning-enhanced version of ODA-Fin-SFT-8B. It achieves **state-of-the-art performance** among open-source financial LLMs of comparable size.
+
+## 📖 Overview
+
+**ODA-Fin-RL-8B** is built on [ODA-Fin-SFT-8B](https://huggingface.co/OpenDataArena/ODA-Fin-SFT-8B) and further optimized via **Group Relative Policy Optimization (GRPO)** on the **ODA-Fin-RL-12K** dataset—a carefully curated subset of 12K hard-but-verifiable financial reasoning tasks. This two-stage training strategy (SFT → RL) achieves optimal performance across diverse financial benchmarks.
+
+### 🎯 Key Highlights
+
+- **Base Model**: ODA-Fin-SFT-8B (Qwen3-8B fine-tuned on 318K CoT samples)
+- **RL Training**: GRPO on ODA-Fin-RL-12K (12K difficulty-filtered samples)
+- **Avg Performance**: 74.6% across 9 financial benchmarks (+2.5 over SFT)
+- **SOTA Achievement**: Highest score among open-source 8B financial LLMs
+- **Key Strengths**:
+  - **Finova: 54.6%** (Best among 8B models, +6.8 over SFT)
+  - **TaTQA: 89.3%** (+2.3 over SFT, +4.2 over Qwen3-32B)
+  - **FPB: 83.4%** (+7.8 over SFT, strong sentiment reasoning)
+
+---
+
+## 🧠 Model Training
+
+
+### Stage 1: Supervised Fine-Tuning (SFT)
+
+- **Dataset**: ODA-Fin-SFT-318K
+- **Method**: Full-parameter fine-tuning
+- **Epochs**: 3
+- **Result**: Establishes strong reasoning foundation (72.1% avg)
+
+### Stage 2: Reinforcement Learning (RL)
+
+- **Dataset**: ODA-Fin-RL-12K (difficulty-filtered: fail rate >= 50%)
+- **Algorithm**: GRPO (Group Relative Policy Optimization)
+- **Training Config**:
+  ```yaml
+  Hardware: 8×NVIDIA H800 (80GB)
+  Batch Size: 256
+  Rollouts per Sample: 4
+  Temperature: 0.6
+  Top-p: 0.85
+  Learning Rate: 1e-6
+  KL Coefficient: 0.001
+  ```
+
+---
+
+## 📊 Model Performance
+
+### Main Results (vs SOTA Baselines)
+
+<figure align="center">
+  <img src="imgs/main_results_table.png" width="100%" alt="p">
+  <figcaption><em>Main Results: ODA-Fin-RL achieves top three performance across most benchmarks. 'FinIQ', 'HL' and 'CFQA' refer to FinanceIQ, Headlines, and ConvFinQA benchmarks.</em></figcaption>
+</figure>
+
+**Performance Highlights**:
+- **Matches Qwen3-32B** (74.7%) with **4× fewer parameters**
+- **+4.3 points** over DianJin-R1-7B (best previous 7B financial LLM)
+- **+2.1 points** over Qwen3-8B-Thinking (larger reasoning model)
+- **Dominates numerical reasoning**: TaTQA (89.3%), FinQA (73.3%), ConvFinQA (80.4%)
+
+---
+
+## 📚 Citation
+
+```bibtex
+@misc{cao2026unlockingdatavaluefinance,
+      title={Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training}, 
+      author={Chuxue Cao and Honglin Lin and Zhanping Zhong and Xin Gao and Mengzhang Cai and Conghui He and Sirui Han and Lijun Wu},
+      year={2026},
+      eprint={2603.07223},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2603.07223}, 
+}
+
+```
+
+---
+
+## 📄 License
+
+This model is released under the [Apache 2.0 License](https://opensource.org/licenses/Apache-2.0). The training data (ODA-Fin-SFT-318K) aggregates from 25+ open-source repositories, each with their own licenses.
+
+---
+
+## 🤝 Acknowledgments
+
+We thank the creators of DianJin-R1-Data, Agentar-DeepFinance-100K, financial_phrasebank, Finance-Instruct-500k, and others. We also thank the Qwen team for the powerful Qwen3 series models.
+
+---
+
+## 🔗 Related Resources
+
+- **SFT Dataset**: [ODA-Fin-SFT-318K](https://huggingface.co/datasets/OpenDataArena/ODA-Fin-SFT-318k)
+- **RL Dataset**: [ODA-Fin-RL-12K](https://huggingface.co/datasets/OpenDataArena/ODA-Fin-RL-12K)
+- **SFT Model**: [ODA-Fin-SFT-8B](https://huggingface.co/OpenDataArena/ODA-Fin-SFT-8B)
+<!-- - **RL Model**: [ODA-Fin-RL-8B](https://huggingface.co/OpenDataArena/ODA-Fin-RL-8B) -->
+
+<!-- - **Paper**: [arXiv:2512.XXXXX](https://arxiv.org/abs/2512.XXXXX) -->