- We aim to expand evaluation to more diverse visual environments
- Future plans include scaling to larger models and applying TRICO to text-only tasks
## Acknowledgement
We thank [RAGEN](https://github.com/RAGEN-AI/RAGEN) for its innovative exploration in multi-turn reinforcement learning for LLM agents. We thank [verl](https://github.com/volcengine/verl) for its RL framework. We thank [EasyR1](https://github.com/hiyouga/EasyR1) for adding initial support for VLMs to verl.
## Citation
If you find this work useful, please cite our paper:
If you find our repo useful, we appreciate it if you could cite our work at:
```
@misc{...
```bibtex
@misc{VAGEN,
title={VAGEN: Training VLM Agents with Multi-Turn Reinforcement Learning},
author={...},
author={Kangrui Wang* and Pingyue Zhang* and Zihan Wang* and Qineng Wang* and Chi Wan and Zhengyuan Yang and Linjie Li and Manling Li},
