ODA-Mixture-500k

Name: ODA-Mixture-500k
Creator: maas
Published: 2026-01-01T13:03:39+08:00

魔搭社区2026-07-08 更新2026-07-15 收录

数据链接：

https://modelscope.cn/datasets/OpenDataArena/ODA-Mixture-500k 数据链接链接失效反馈

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资源简介：

# ODA-Mixture-500k <img src="performance.png" alt="Subject Distribution" width="1200" /> ODA-Mixture-500k is a large-scale general-purpose post-training dataset curated from top-performing open corpora (selected via the *OpenDataArena* leaderboard) and refined through deduplication, benchmark decontamination. --- ## 🧠 Dataset Summary - **Domain**: General-purpose(e.g., Math, Code, Reasoning, General). - **Format**: Problem → Solution (reasoning trace) → Final answer. - **Scale (selected training set)**: ~**500K** samples. - **Goal**: Achieve maximum general-purpose performance gains across various domains (Math, Code, Reasoning, etc.) using a curated dataset of ~500K samples. --- ## ⚙️ Data Curation Pipeline ODA-Mixture-500k is built by following a single rule: **trust the OpenDataArena leaderboard**. ### 1️⃣ Data Collection We utilize **LIMO** as the foundational anchor due to its exceptional sample efficiency on the ODA overall leaderboard. To scale up to 500K, we integrate the top-performing and efficient corpora from specific ODA domain leaderboards, including **AM-Thinking-v1-Distilled-Math** for Math domain, **AM-Thinking-v1-Distilled-code** for Code domain, **math-gpt-4o-200k** for General domain, and **SYNTHETIC-2-SFT-verified** for Reasoning domain. ### 2️⃣ Deduplication & Decontamination We first perform **exact deduplication** over all questions to remove identical items, and then run **benchmark decontamination** to reduce evaluation leakage by removing overlaps with standard and competition benchmarks. ### 3️⃣ Data Selection At the 500K scale, our priority the distributional coverage. We employ semantic clustering to partition the total data pool into distinct thematic clusters. Within each cluster, we perform uniform sampling to ensure the final mixture represents a broad and balanced spectrum of reasoning tasks, maximizing the model's generalization capabilities. --- ## 📚 Source Composition | Source | Count | Percentage | |---|---:|---:| | LIMO | 817 | 0.16% | | AM-Thinking-Distilled-math | 150,244 | 29.67% | | AM-Thinking-Distilled-code| 150,252 | 29.67% | | math-gpt-4o-200k| 100,138 | 19.78% | | SYNTHETIC-2-SFT-verified| 104,913 | 20.72% | --- ## 🧩 Data Format ```json { "id": "unique_identifier", "source": "data source", "question": "textual question or instruction", "response": "textual response" } ``` --- ## 📈 Performance ODA-Mixture-500k is evaluated as an SFT corpus for both **Qwen2.5-7B-Base** and **Qwen3-8B-Base**. Across the full ODA benchmark suite spanning four domains—**General (DROP, IFEVAL, AGIEVAL, MMLU-Pro)**, **Math (GSM8K, MATH500, Omni-Math, OlympiadBench, AIME2024)**, **Code (HumanEval, MBPP, LCB (V5), HumanEval+)**, and **Reasoning (ARC-C, BBH, CALM, KOR-BENCH)**—we observe consistent improvements over the corresponding base checkpoints, with particularly strong gains on several benchmarks. <div style="overflow-x: auto; font-family: sans-serif; margin-bottom: 20px;"> <table style="width: 100%; border-collapse: collapse; text-align: center; font-size: 14px; min-width: 900px; color: inherit;"> <caption style="padding: 10px; font-weight: bold;"> Leaderboard Performance Comparison. Best scores in bold, second-best underlined. Eff. denotes Data Efficiency. </caption> <thead> <tr style="border-top: 2px solid currentColor; border-bottom: 1px solid currentColor;"> <th style="text-align: left; padding: 8px;">Dataset</th> <th>Size</th> <th>Eff.</th> <th>General</th> <th>Math</th> <th>Code</th> <th>Reasoning</th> <th style="border-left: 1px solid rgba(128, 128, 128, 0.3);">AVG</th> </tr> </thead> <tbody>  <tr style="background-color: rgba(128, 128, 128, 0.08); font-weight: bold;"> <td colspan="8" style="text-align: center; padding: 10px 8px; letter-spacing: 1px;">Qwen2.5-7B-Base</td> </tr> <tr> <td style="text-align: left; padding: 8px;">Qwen2.5-7B-Base</td> <td>-</td><td>-</td> <td>51.4</td><td>39.8</td><td>50.1</td><td>42.7</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">46.0</td> </tr> <tr> <td style="text-align: left; padding: 8px;">OpenThoughts3-1.2M</td> <td>1.2M</td><td>+0.011</td> <td>45.5</td><td>71.8</td><td>67.0</td><td>54.3</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">59.6</td> </tr> <tr> <td style="text-align: left; padding: 8px;">OmniThought-0528</td> <td>365k</td><td>+0.027</td> <td>47.1</td><td>71.2</td><td>47.6</td><td>57.2</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">55.8</td> </tr> <tr> <td style="text-align: left; padding: 8px;">SYNTHETIC-2-SFT-verified</td> <td>105k</td><td>+0.086</td> <td>51.3</td><td>69.8</td><td>40.1</td><td>58.9</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">55.0</td> </tr> <tr> <td style="text-align: left; padding: 8px;">AM-Thinking-v1-Distilled-math</td> <td>558k</td><td>+0.016</td> <td>57.7</td><td>77.4</td><td>39.5</td><td>44.8</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">54.8</td> </tr> <tr> <td style="text-align: left; padding: 8px;">LIMO</td> <td>817</td><td>+9.920</td> <td>60.7</td><td>44.0</td><td>57.9</td><td>53.8</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">54.1</td> </tr> <tr> <td style="text-align: left; padding: 8px;">MiroMind-M1-SFT-719K</td> <td>719k</td><td>+0.006</td> <td>52.0</td><td>71.0</td><td>26.3</td><td>51.5</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">50.2</td> </tr> <tr> <td style="text-align: left; padding: 8px;">AM-Thinking-v1-Distilled-code</td> <td>324k</td><td>+0.024</td> <td>49.9</td><td>52.3</td><td>68.7</td><td>44.4</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">53.8</td> </tr> <tr> <td style="text-align: left; padding: 8px;">Light-R1-SFTData</td> <td>79k</td><td>+0.084</td> <td>55.5</td><td>64.4</td><td>38.8</td><td>51.9</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">52.7</td> </tr> <tr style="background-color: rgba(128, 128, 128, 0.18); font-weight: bold;"> <td style="text-align: left; padding: 8px;">ODA-Mixture-500k</td> <td>500k</td><td>+0.039</td> <td>63.4</td><td>72.8</td><td>66.7</td><td>59.6</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">65.6</td> </tr> <tr style="background-color: rgba(128, 128, 128, 0.18); font-weight: bold;"> <td style="text-align: left; padding: 8px;">ODA-Mixture-100k</td> <td>100k</td><td>+0.149</td> <td>56.8</td><td>71.2</td><td>64.4</td><td>51.5</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">61.0</td> </tr>  <tr style="border-top: 1px solid currentColor; background-color: rgba(128, 128, 128, 0.08); font-weight: bold;"> <td colspan="8" style="text-align: center; padding: 10px 8px; letter-spacing: 1px;">Qwen3-8B-Base</td> </tr> <tr> <td style="text-align: left; padding: 8px;">Qwen3-8B-Base</td> <td>-</td><td>-</td> <td>58.7</td><td>51.2</td><td>52.4</td><td>50.6</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">53.2</td> </tr> <tr> <td style="text-align: left; padding: 8px;">MiroMind-M1-SFT-719K</td> <td>719k</td><td>+0.023</td> <td>64.5</td><td>77.2</td><td>63.6</td><td>65.8</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">67.8</td> </tr> <tr> <td style="text-align: left; padding: 8px;">AM-Thinking-v1-Distilled-math</td> <td>558k</td><td>+0.028</td> <td>65.9</td><td>79.7</td><td>59.5</td><td>63.2</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">67.1</td> </tr> <tr> <td style="text-align: left; padding: 8px;">OmniThought-0528</td> <td>365k</td><td>+0.043</td> <td>55.8</td><td>78.3</td><td>68.1</td><td>66.0</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">67.0</td> </tr> <tr> <td style="text-align: left; padding: 8px;">AM-Thinking-v1-Distilled-code</td> <td>324k</td><td>+0.045</td> <td>64.8</td><td>64.9</td><td>75.8</td><td>59.3</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">66.2</td> </tr> <tr> <td style="text-align: left; padding: 8px;">Light-R1-SFTData</td> <td>79k</td><td>+0.168</td> <td>64.9</td><td>71.8</td><td>59.0</td><td>63.6</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">64.8</td> </tr> <tr> <td style="text-align: left; padding: 8px;">SYNTHETIC-2-SFT-verified</td> <td>105k</td><td>+0.107</td> <td>59.5</td><td>75.4</td><td>56.1</td><td>66.6</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">64.4</td> </tr> <tr> <td style="text-align: left; padding: 8px;">LIMO</td> <td>817</td><td>+0.490</td> <td>61.7</td><td>46.0</td><td>52.7</td><td>54.1</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">53.6</td> </tr> <tr style="background-color: rgba(128, 128, 128, 0.18); font-weight: bold;"> <td style="text-align: left; padding: 8px;">ODA-Mixture-500k</td> <td>500k</td><td>+0.042</td> <td>71.2</td><td>77.2</td><td>73.0</td><td>69.7</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">72.8</td> </tr> <tr style="background-color: rgba(128, 128, 128, 0.18); font-weight: bold; border-bottom: 2px solid currentColor;"> <td style="text-align: left; padding: 8px;">ODA-Mixture-100k</td> <td>100k</td><td>+0.177</td> <td>61.1</td><td>77.3</td><td>73.2</td><td>64.7</td> <td style="border-left: 1px solid rgba(128, 128, 128, 0.3);">69.0</td> </tr> </tbody> </table> </div> --- ## 🌐 About OpenDataArena [OpenDataArena](https://arena.opendatalab.org.cn/) is an open research platform dedicated to **discovering, evaluating, and advancing high-quality datasets for AI post-training**. It provides a transparent, data-centric ecosystem to support reproducible dataset evaluation and sharing. **Key Features:** - 🏆 **Dataset Leaderboard** — helps researchers identify **the most valuable and high-quality datasets across different domains**. - 📊 **Detailed Evaluation Scores** — provides **comprehensive metrics** to assess data quality, complexity, difficulty etc. - 🧰 **Data Processing Toolkit** — [OpenDataArena-Tool](https://github.com/OpenDataArena/OpenDataArena-Tool) offers an open-source pipeline for dataset curation and scoring. If you find our work helpful, please consider **⭐ starring and subscribing** to support our research. --- ## 📚 Citation ```bibtex @article{gao2025closing, title={Closing the Data Loop: Using OpenDataArena to Engineer Superior Training Datasets}, author={Gao, Xin and Wang, Xiaoyang and Zhu, Yun and Cai, Mengzhang and He, Conghui and Wu, Lijun}, journal={arXiv preprint arXiv:2601.09733}, year={2025} } ``` ```bibtex @article{cai2025opendataarena, title={OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Value}, author={Cai, Mengzhang and Gao, Xin and Li, Yu and Lin, Honglin and Liu, Zheng and Pan, Zhuoshi and Pei, Qizhi and Shang, Xiaoran and Sun, Mengyuan and Tang, Zinan and others}, journal={arXiv preprint arXiv:2512.14051}, year={2025} } ```

提供机构：

maas

创建时间：

2026-01-01

ODA-Mixture-500k

资源简介：

相关数据集