Improve model card and add metadata

README.md

@@ -1,41 +1,40 @@
 ---
 license: mit
+library_name: transformers
+pipeline_tag: text-generation
 ---
 
+# RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System
 
-# Introduction to TraDo
+[Paper](https://huggingface.co/papers/2602.02488) | [Code](https://github.com/Gen-Verse/Open-AgentRL) | [Blog](https://yinjjiew.github.io/projects/rlanything/) | [Project Page](https://huggingface.co/collections/Gen-Verse/open-agentrl)
 
-[Paper](https://arxiv.org/abs/2602.02488) | [Code](https://github.com/Gen-Verse/Open-AgentRL) | [Blog](https://yinjjiew.github.io/projects/rlanything/)
+**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 any LLM or agentic scenarios. 
 
-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.
+Specifically, the policy is trained with integrated feedback from step-wise and outcome signals, while the reward model is jointly optimized via consistency feedback, which in turn further improves policy training. Moreover, theory-motivated automatic environment adaptation improves training for both the reward and policy models by leveraging critic feedback from each, enabling learning from experience.
 
+## Key Features
 * **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.
-
-
+* **Critic Feedback for Environment:** 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>
 
+## 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}
 }
-```
-
-
+```