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README.md

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-license: mit
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+---
+license: mit
+---
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+# Introduction to TraDo
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+[Paper](https://arxiv.org/abs/2509.06949) | [Code](https://github.com/Gen-Verse/Open-AgentRL) | [Blog](https://yinjjiew.github.io/projects/rlanything/)
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+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.
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+* **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.
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+
+
+<p align="center">
+  <img src="https://github.com/yinjjiew/Data/raw/main/rlanything/rlanythingoverview.png" width="100%"/>
+</p>
+
+
+<p align="center">
+  <img src="https://github.com/yinjjiew/Data/raw/main/rlanything/rlanythingmaintable.png" width="100%"/>
+</p>
+
+
+
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+# Citation
+
+```
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+```
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+