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README.md

@@ -1,8 +1,35 @@
 ---
 license: mit
+library_name: transformers
+pipeline_tag: text-generation
 ---
-### Self-Certainty: Llama-3.2-3B-Instruct trained on DAPO-14k
 
-This is the Llama-3.2-3B-Instruct model trained by Self-Certainty Maximization using DAPO-14k 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 **Self-Certainty: Llama-3.2-3B-Instruct trained on DAPO-14k** model. This is a Llama-3.2-3B-Instruct model trained by Self-Certainty Maximization using the DAPO-14k training set, as part of the broader **Co-rewarding** framework.
+
+**Co-rewarding** is a novel self-supervised reinforcement learning (RL) framework designed to improve the reasoning ability of large language models (LLMs) while enhancing training stability. It addresses the training collapse issue often encountered in single-view self-rewarding methods by seeking complementary supervision from multiple views. The framework is instantiated in two ways:
+1.  **Co-rewarding-I**: A data-side approach deriving reward signals from contrastive agreement across semantically analogous questions.
+2.  **Co-rewarding-II**: A model-side approach maintaining a slowly-updated reference teacher with pseudo labels to realize self-distillation.
+
+Empirically, Co-rewarding exhibits stable training and outperforms other self-rewarding baselines, significantly improving performance on mathematical reasoning benchmarks.
+
+## Paper
+The model was presented in the paper:
+[**Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models**](https://huggingface.co/papers/2508.00410)
+
+## Code and Further Information
+For detailed installation instructions, training scripts, datasets, and further information on the **Co-rewarding** framework, 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}
+}
+```