Duplicate from sojuL/RubricHub_v1

Co-authored-by: Sunzhu Li <sojuL@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+language:
+- zh
+- en
+tags:
+- medical
+- science
+- wirting
+- isntruction
+- chat
+- general
+pretty_name: RubricHub
+size_categories:
+- 100K<n<1M
+task_categories:
+- text-generation
+- reinforcement-learning
+- question-answering
+---
+
+
+# RubricHub
+<div align="center">
+
+<!-- You can add badges here later, e.g., ArXiv, License, HuggingFace -->
+<a href="https://arxiv.org/abs/2601.08430">
+    <img src="https://img.shields.io/badge/arXiv-2601.08430-b31b1b.svg" alt="Paper"/>
+</a>
+<a href="https://huggingface.co/datasets/sojuL/RubricHub_v1">
+    <img src="https://img.shields.io/badge/Data-HuggingFace-yellow" alt="RubricHub"/>
+</a>
+<a href="https://github.com/teqkilla/RubricHub/">
+    <img src="https://img.shields.io/badge/Code-GitHub-black?logo=github" alt="GitHub"/>
+</a>
+<a href="LICENSE">
+    <img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License"/>
+</a>
+
+</div>
+
+
+RubricHub is a large-scale (approximately 110K), multi-domain dataset that provides high-quality rubric-based supervision for open-ended generation tasks. It is constructed via an automated coarse-to-fine rubric generation framework, which integrates principle-guided synthesis, multi-model aggregation, and difficulty evolution to produce comprehensive and highly discriminative evaluation criteria, overcoming the supervision ceiling of coarse or static rubrics. Leveraging RubricHub in a two-stage post-training pipeline (RuFT + RuRL) yields substantial gains in open-ended reasoning, enabling Qwen3-14B to achieve state-of-the-art performance of 69.3 on HealthBench, surpassing multiple proprietary frontier models.
+
+
+
+## 📢 News
+*   **[2026-02-03]** 🔥 **Training recipes (external) are now available.**  For RuRL, refer to [RuscaRL](https://github.com/IANNXANG/RuscaRL) (sync) or [verl-rubric](https://github.com/DoYangTan/verl-rubric) (async). For RuFT/SFT, refer to [LlamaFactory](https://github.com/hiyouga/LlamaFactory). Our RubricHub rule-based scorer/grader integration for RuRL (incl. instruction-following rules) is being cleaned up and will be released soon.
+*   **[2026-02-03]** 🔥 **Data synthesis code released.** See `data_synthesis_final/README.md`. 
+*   **[2026-01-17]** RubricHub dataset is released, see https://huggingface.co/datasets/sojuL/RubricHub_v1.
+*   **[2026-01-12]** RubricHub paper is released, see https://arxiv.org/abs/2601.08430.
+
+
+
+## Schema
+**RuRL**
+- **prompt** (list of dict)：A list of prompts formatted for VeRL training.
+- **data_source** (string)：A string indicating the origin of the data.
+- **ability** (string)：The primary capability or skill dimension targeted by this sample.
+- **reward_model** (dict)：A Verl-compatible reward specification dictionary that defines how the response is evaluated, including *ground_truth*, *rubrics*, and *style*.
+- **extra_info** (dict)：Auxiliary VeRL-formatted metadata that mirrors or extends the main *prompt* and *reward_model*.
+- **Rubrics** (list of dict)：Each consisting of explicit scoring criteria and corresponding weights.
+
+
+**RuFT**
+- **source** (string)：Indicates the origin data source.
+- **query** (string)：The input prompt or instruction presented to the model.
+- **answer** (string)：The model-generated or reference response corresponding to the query.
+- **sample_id** (string)：A unique identifier for each data sample used for tracking and indexing.
+- **rubrics** (list of dict)：A set of evaluation rubrics, where each rubric defines a scoring criterion and its associated weight.
+- **rubric_score** (float)：The aggregated score of the answer computed by combining weighted rubric scores.
+- **rubric_judge_details** (list of dict)：Detailed evaluation results produced by applying the rubrics to the answer, including per-criterion scores and judgments.
+
+## Citation
+If you use this dataset, please cite:
+
+```bibtex
+@article{li2026rubrichub,
+  title={RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation},
+  author={Li, Sunzhu and Zhao, Jiale and Wei, Miteto and Ren, Huimin and Zhou, Yang and Yang, Jingwen and Liu, Shunyu and Zhang, Kaike and Chen, Wei},
+  journal={arXiv preprint arXiv:2601.08430},
+  year={2026}
+}

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