Improve model card: Add pipeline tag, library name, and paper link

README.md

@@ -1,8 +1,35 @@
 ---
 license: mit
+library_name: transformers
+pipeline_tag: text-generation
 ---
-### Self-Certainty: Qwen3-4B-Base trained on DAPO-14k
 
-This is the Qwen3-4B-Base model trained by Self-Certainty Maximization using DAPO-14k training set.
+# Self-Certainty: Qwen3-4B-Base trained on DAPO-14k
 
-If you are interested in Co-rewarding, you can find more details on our Github Repo [https://github.com/tmlr-group/Co-rewarding].
+This model is a Qwen3-4B-Base checkpoint trained by **Self-Certainty Maximization** using the DAPO-14k dataset, as part of the research presented in the paper [Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models](https://huggingface.co/papers/2508.00410).
+
+The "Co-rewarding" framework is a novel self-supervised reinforcement learning (RL) framework designed to improve training stability by seeking complementary supervision from multiple views, addressing common challenges in self-rewarding methods for Large Language Models (LLMs). This specific model contributes to eliciting stronger reasoning abilities, particularly on mathematical reasoning benchmarks.
+
+**Paper:** [Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models](https://huggingface.co/papers/2508.00410)
+**Code Repository:** https://github.com/tmlr-group/Co-rewarding
+
+---
+
+## Model Description
+
+This is the Qwen3-4B-Base model trained by Self-Certainty Maximization using the DAPO-14k training set.
+
+For more details on the Co-rewarding framework, training procedures, and other checkpoints, please refer to the [Github Repository](https://github.com/tmlr-group/Co-rewarding).
+
+---
+
+## Citation
+If you use our datasets or models, please cite our paper!
+```
+@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}
+}
+```