Introduction to TraDo
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.
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}
}