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fcc38f9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | # Welcome to VAGEN Documentation!
## Introduction
VAGEN is a multi-turn reinforcement learning framework designed for training Visual Language Model (VLM) agents efficiently.
## Document Structure
### Quick Strat
- [Installation and Run Experiment](run-exp.md): Get VAGEN up and running
### Configurations
- [General Configuration](configs/general-config.md): Understanding VAGEN's configuration system
- [Algorithm Configuration](configs/algo-config.md): Configure different algorithms
### Environments
- [Create your Own Environment](envs/create-env.md): Build custom environments
- [Create your Own Service](envs/create-service.md): Scale your training infrastructure
#### Comparison of Algorithms
| **Feature** | **PPO & GRPO** | **VAGEN-Base** | **VAGEN-Full** |
| --- | --- | --- | --- |
| **Sequence Structure** | Single response | Multiple turn interaction | Multiple turn interaction |
| **LM output** | No special structure | `<think>...</think><ans>...</ans>` | `<think>...</think><ans>...</ans><eoa>` |
| **Discounting** | Single discount rate | Single discount rate | Bi-level discounting |
| **Optimization** | All tokens equally | All tokens equally | Selective token optimization |
## Citation
If you find VAGEN useful, we appreciate it if you could cite our work at:
```bibtex
@misc{wang2025vagen,
title={Reinforcing Visual State Reasoning for Multi-Turn VLM Agents},
author={Kangrui Wang* and Pingyue Zhang* and Zihan Wang* and Yaning Gao* and Linjie Li* and Qineng Wang and Hanyang Chen and Chi Wan and Yiping Lu and Zhengyuan Yang and Lijuan Wang and Ranjay Krishna and Jiajun Wu and Li Fei-Fei and Yejin Choi and Manling Li},
year={2025},
url={https://github.com/RAGEN-AI/VAGEN}
}
```
## License
Licensed under the MIT License. |