| # Welcome to VAGEN Documentation! |
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| ## Introduction |
| VAGEN is a multi-turn reinforcement learning framework designed for training Visual Language Model (VLM) agents efficiently. |
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| ## Document Structure |
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| ### Quick Strat |
| - [Installation and Run Experiment](run-exp.md): Get VAGEN up and running |
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| ### Configurations |
| - [General Configuration](configs/general-config.md): Understanding VAGEN's configuration system |
| - [Algorithm Configuration](configs/algo-config.md): Configure different algorithms |
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| ### Environments |
| - [Create your Own Environment](envs/create-env.md): Build custom environments |
| - [Create your Own Service](envs/create-service.md): Scale your training infrastructure |
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| #### Comparison of Algorithms |
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|
| | **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 | |
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| ## Citation |
| If you find VAGEN useful, we appreciate it if you could cite our work at: |
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|
| ```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} |
| } |
| ``` |
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| ## License |
| Licensed under the MIT License. |
