Add metadata and improve model card

Hi! I'm Niels from the Hugging Face community science team.

I noticed this model card could use some additional metadata and documentation to help users discover and use it. This PR:
- Adds the `pipeline_tag: image-text-to-text` and `library_name: transformers` metadata.
- Updates the title and description to align with the official paper: **RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System**.
- Ensures links to the paper, code repository, and project blog are correctly maintained.

Please let me know if you have any questions!

README.md

@@ -1,20 +1,19 @@
 ---
 license: mit
+library_name: transformers
+pipeline_tag: image-text-to-text
 ---
 
-
-# Introduction to TraDo
+# RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System
 
 [Paper](https://arxiv.org/abs/2602.02488) | [Code](https://github.com/Gen-Verse/Open-AgentRL) | [Blog](https://yinjjiew.github.io/projects/rlanything/)
 
-We introduce **RLAnything**, a reinforcement learning framework forges environment, policy and reward model in a completely dynamic system to enhance the training signals and improve the whole system.
+We introduce **RLAnything**, a reinforcement learning framework that forges environment, policy, and reward models in a completely dynamic system to enhance the training signals and improve the whole system.
 
 * **Integrated Feedback for Policy:** The policy is trained with integrated outcome and step-wise signals from reward model.
 * **Consistency Feedback for Reward Model:** The Reward model is jointly optimized by consistency feedback, further improves policy training.
 * **Critic Feedback for Environment:** Our theory-motivated automatic environment adaptation improves training for both the reward and policy models by leveraging critic feedback from each.
 
-
-
 <p align="center">
   <img src="https://github.com/yinjjiew/Data/raw/main/rlanything/rlanythingoverview.png" width="100%"/>
 </p>
@@ -30,16 +29,13 @@ We introduce **RLAnything**, a reinforcement learning framework forges environme
 </p>
 
 
-
 # Citation
 
-```
+```bibtex
 @article{wang2026rlanything,
   title={RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System},
-  author={Wang, Yinjie and Xie, Tianbao and Shen, Ke and Wang, Mengdi and Yang, Ling},
+  author={Wang, Yinjie and Xie, Tianbao Clerk and Shen, Ke and Wang, Mengdi and Yang, Ling},
   journal={arXiv preprint arXiv:2602.02488},
   year={2026}
 }
-```
-
-
+```