初始化项目，由ModelHub XC社区提供模型

Model: xx18/Baseline-4B-MATH12K
Source: Original Platform

README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Composition-RL-8B
+
+[**Paper**](https://huggingface.co/papers/2602.12036) | [**Code**](https://github.com/XinXU-USTC/Composition-RL)
+
+**Composition-RL-8B** is a large language model fine-tuned for enhanced reasoning using the Composition-RL framework. It was initialized from the **Qwen3-8B-Base** architecture and trained on the **MATH-Composition-199K** dataset.
+
+## Description
+
+Composition-RL is a data-efficient Reinforcement Learning with Verifiable Rewards (RLVR) approach presented in the paper [Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models](https://huggingface.co/papers/2602.12036). 
+
+The method addresses the issue of "too-easy" prompts (where pass rates reach 1) that occur as training progresses, which reduces the effective training signal. Composition-RL automatically composes multiple verifiable problems into a single, more challenging compositional prompt, ensuring the model continues to receive informative rewards throughout the reinforcement learning process.
+
+- **Base Model:** Qwen3-8B-Base
+- **Training Method:** Reinforcement Learning with Verifiable Rewards (RLVR)
+- **Training Dataset:** [MATH-Composition-199K](https://huggingface.co/datasets/xx18/MATH-Composition-199K)
+
+## 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 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}
+}
+```