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---
license: mit
---
# Introduction to TraDo
[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.
<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>
# Citation
```
@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}
}
```
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