Improve model card and add metadata

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

I noticed this model card could be improved with additional metadata and clearer links to the research paper. This PR updates the model card to:
- Add `pipeline_tag: text-generation` and `library_name: transformers` metadata for better discoverability and integration.
- Link the model to its research paper: [RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System](https://huggingface.co/papers/2602.02488).
- Correct the model name from "TraDo" to **RLAnything** as per the official repository.
- Include a direct link to the code on GitHub and the project blog.
- Add an overview of the key features and performance highlights.

Please let me know if you have any questions!

README.md

@@ -1,41 +1,38 @@
 ---
 license: mit
+library_name: transformers
+pipeline_tag: text-generation
 ---
 
-
-# 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.
-
-* **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.
-
+**RLAnything** is a reinforcement learning framework that dynamically forges environment, policy, and reward models through closed-loop optimization, amplifying learning signals and strengthening the overall RL system for LLM and agentic scenarios.
 
+## Key Features
+* **Integrated Feedback for Policy:** The policy is trained with integrated feedback from step-wise and outcome signals, rather than outcome only.
+* **Consistency Feedback for Reward Model:** The reward model is jointly optimized via consistency feedback, which in turn further improves policy training.
+* **Critic Feedback for Environment:** Theory-motivated automatic environment adaptation improves training for both the reward and policy models by leveraging critic feedback from each, enabling learning from experience.
 
 <p align="center">
   <img src="https://github.com/yinjjiew/Data/raw/main/rlanything/rlanythingoverview.png" width="100%"/>
 </p>
 
+## Results
+Empirically, each added component consistently improves the overall system, and RLAnything yields substantial gains across various representative LLM and agentic tasks, boosting Qwen3-VL-8B-Thinking by 9.1% on OSWorld and Qwen2.5-7B-Instruct by 18.7% and 11.9% on AlfWorld and LiveBench, respectively.
 
 <p align="center">
   <img src="https://github.com/yinjjiew/Data/raw/main/rlanything/rlanythingmaintable.png" width="100%"/>
 </p>
 
+## Citation
 
-
-
-# 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},
   journal={arXiv preprint arXiv:2602.02488},
   year={2026}
 }
-```
-
-
+```