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.
## References
[RAGEN](https://github.com/RAGEN-AI/RAGEN): Training Agents by Reinforcing Reasoning
[verl](https://www.notion.so/VAGEN-Training-VLM-Agents-with-Multi-Turn-Reinforcement-Learning-1bfde13afb6e80b792f6d80c7c2fcad0?pvs=21): Volcano Engine Reinforcement Learning for LLM
[Search-R1](https://github.com/PeterGriffinJin/Search-R1): Train your LLMs to reason and call a search engine with reinforcement learning
[Agent-R1](https://github.com/0russwest0/Agent-R1): Training Powerful LLM Agents with End-to-End Reinforcement Learning
[OpenManus-RL](https://github.com/OpenManus/OpenManus-RL): A live stream development of RL tunning for LLM agents
## Citation
If you find our repo useful, we appreciate it if you could cite our work at:
