Add model card and metadata

README.md

@@ -0,0 +1,33 @@
+---
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Composition-RL-8B
+
+Composition-RL is a data-efficient Reinforcement Learning with Verifiable Rewards (RLVR) approach that addresses the scarcity of informative training signals by automatically composing multiple verifiable problems into a single, harder compositional prompt.
+
+This specific checkpoint is the 8B version, initialized from **Qwen3-8B-Base** and trained on the `MATH-Composition-199K` dataset.
+
+## Model Description
+As training progresses in RLVR, models often master "easy" prompts, resulting in a pass rate of 1 and reducing effective learning. Composition-RL mitigates this by creating new, complex, yet verifiable questions from existing data, maintaining a high level of difficulty and informative signals throughout training.
+
+- **Developed by:** Xin Xu, Clive Bai, Kai Yang, Tianhao Chen, Yangkun Chen, Weijie Liu, Hao Chen, Yang Wang, Saiyong Yang, and Can Yang.
+- **Paper:** [Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models](https://huggingface.co/papers/2602.12036)
+- **Repository:** [GitHub - Composition-RL](https://github.com/XinXU-USTC/Composition-RL)
+- **Base Model:** Qwen3-8B-Base
+
+## Usage
+For evaluation and data generation instructions, please refer to the official [GitHub repository](https://github.com/XinXU-USTC/Composition-RL).
+
+## Citation
+If you find this work helpful for your research, please consider citing:
+
+```bibtex
+@article{xu2026composition-rl,
+  title={Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models},
+  author={Xu, Xin and Bai, Clive and Yang, Kai Rural and Chen, Tianhao and Chen, Yangkun and Liu, Weijie and Chen, Hao and Wang, Yang and Yang, Saiyong and Yang, Can},
+  journal={arXiv preprint arXiv:2602.12036},
+  year={2026}
+}
+```