Improve model card: Add metadata, paper link, and detailed description (#1)

- Improve model card: Add metadata, paper link, and detailed description (c50c73366b00b5e4d8d8f6f2a552159de430ddf0)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,5 +1,26 @@
-### Co-rewarding-I: Qwen3-8B-Base trained on OpenRS
+---
+pipeline_tag: text-generation
+library_name: transformers
+license: apache-2.0
+---
 
-This is the Qwen3-8B-Base model trained by Co-rewarding-I using OpenRS training set.
+# Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models
 
-If you are interested in Co-rewarding, you can find more details on our Github Repo [https://github.com/tmlr-group/Co-rewarding].
+This repository contains the **Co-rewarding-I: Qwen3-8B-Base** model, trained on the OpenRS dataset. This model is an instantiation of the **Co-rewarding** framework, a novel self-supervised reinforcement learning (RL) approach presented in the paper [Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models](https://huggingface.co/papers/2508.00410).
+
+Co-rewarding aims to improve the reasoning ability of large language models (LLMs) by enhancing training stability. It addresses the common "training collapse" issue found in single-view self-rewarding methods by seeking complementary supervision from multiple perspectives. Specifically, Co-rewarding-I uses a data-side instantiation that derives reward signals from contrastive agreement across semantically analogous questions. The method has shown stable training and significant performance improvements over other self-rewarding baselines on various mathematical reasoning benchmarks.
+
+For more details, including installation instructions, training scripts, additional checkpoints, and evaluation procedures, please refer to the official GitHub repository:
+[https://github.com/tmlr-group/Co-rewarding](https://github.com/tmlr-group/Co-rewarding)
+
+## Citation
+
+If you use our datasets or models, please cite our paper:
+```bibtex
+@article{zhang2025co,
+  title={Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models},
+  author={Zhang, Zizhuo and Zhu, Jianing and Ge, Xinmu and Zhao, Zihua and Zhou, Zhanke and Li, Xuan and Feng, Xiao and Yao, Jiangchao and Han, Bo},
+  journal={arXiv preprint arXiv:2508.00410},
+  year={2025}
+}
+```