Improve model card: Add metadata, paper link, and correct GitHub link

This PR enhances the model card for `TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base` by:
- Adding `pipeline_tag: text-generation` for improved discoverability on the Hub, as it's a causal language model.
- Adding `library_name: transformers` to enable the automated "How to use" widget, based on its `Qwen3ForCausalLM` architecture and `transformers_version` in `config.json`.
- Including relevant `tags` such as `qwen3`, `fine-tuning`, `reinforcement-learning`, `reasoning`, `mathematical-reasoning`, and `self-supervised-learning`.
- Integrating a direct link to the paper: [Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models](https://huggingface.co/papers/2508.00410).
- Correcting the GitHub repository link to [https://github.com/tmlr-group/Co-rewarding](https://github.com/tmlr-group/Co-rewarding), which was previously outdated.
- Clarifying the model's description, noting its role as a baseline model trained with the Self-Certainty method within the context of the Co-rewarding paper.
- No sample usage is included, as no direct inference snippet was found in the provided GitHub README content, adhering to the specified guidelines.

README.md

@@ -1,19 +1,34 @@
 ---
 license: mit
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - qwen3
+  - fine-tuning
+  - reinforcement-learning
+  - reasoning
+  - mathematical-reasoning
+  - self-supervised-learning
 ---
+
+# Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models
+
 ## TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base
 
-This is the Qwen3-1.7B-Base model trained by Self-Certainty method using MATH training set.
+This is the Qwen3-1.7B-Base model trained by the **Self-Certainty** method using the MATH training set. This model serves as a baseline for comparison within the framework presented in the paper [Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models](https://huggingface.co/papers/2508.00410).
+
+The paper "Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models" introduces a novel self-supervised reinforcement learning framework that enhances training stability by incorporating complementary supervision signals.
 
-If you are interested in Co-Reward, you can find more details on our Github Repo [https://github.com/tmlr-group/Co-Reward].
+For more details on the Co-rewarding framework, training procedures, and other released models and datasets, please refer to the official GitHub repository: [https://github.com/tmlr-group/Co-rewarding](https://github.com/tmlr-group/Co-rewarding).
 
 ## Citation
 
-```
-@article{zhang2025coreward,
-      title={Co-Reward: Self-supervised Reinforcement Learning for Large Language Model Reasoning via Contrastive Agreement}, 
-      author={Zizhuo Zhang and Jianing Zhu and Xinmu Ge and Zihua Zhao and Zhanke Zhou and Xuan Li and Xiao Feng and Jiangchao Yao and Bo Han},
-      journal={arXiv preprint arXiv:2508.00410}
-      year={2025},
+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}
 }
 ```