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+# EasyR1: An Efficient, Scalable, Multi-Modality RL Training Framework
+
+[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/EasyR1)](https://github.com/hiyouga/EasyR1/stargazers)
+[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
+
+### Used by [Amazon Web Services](https://aws.amazon.com/cn/blogs/china/building-llm-model-hub-based-on-llamafactory-and-easyr1/)
+
+This project is a clean fork of the original [veRL](https://github.com/volcengine/verl) project to support vision language models, we thank all the authors for providing such a high-performance RL training framework.
+
+EasyR1 is efficient and scalable due to the design of **[HybirdEngine](https://arxiv.org/abs/2409.19256)** and the latest release of **[vLLM](https://github.com/vllm-project/vllm)**'s SPMD mode.
+
+## Features
+
+- Supported models
+  - Llama3/Qwen2/Qwen2.5/Qwen3 language models
+  - Qwen2/Qwen2.5-VL vision language models
+  - DeepSeek-R1 distill models
+
+- Supported algorithms
+  - GRPO
+  - DAPO
+  - Reinforce++
+  - ReMax
+  - RLOO
+
+- Supported datasets
+  - Any text, vision-text dataset in a [specific format](#custom-dataset)
+
+- Supported tricks
+  - Padding-free training
+  - Resuming from checkpoint
+  - Wandb & SwanLab & Mlflow & Tensorboard tracking
+
+## Requirements
+
+### Software Requirements
+
+- Python 3.9+
+- transformers>=4.51.0
+- flash-attn>=2.4.3
+- vllm>=0.8.3
+
+We provide a [Dockerfile](./Dockerfile) to easily build environments.
+
+We recommend using the [pre-built docker image](https://hub.docker.com/r/hiyouga/verl) in EasyR1.
+
+```bash
+docker pull hiyouga/verl:ngc-th2.7.0-cu12.6-vllm0.9.1
+docker run -it --ipc=host --gpus=all hiyouga/verl:ngc-th2.7.0-cu12.6-vllm0.9.1
+```
+
+If your environment does not support Docker, you can consider using **Apptainer**:
+
+```bash
+apptainer pull easyr1.sif docker://hiyouga/verl:ngc-th2.7.0-cu12.6-vllm0.9.1
+apptainer shell --nv --cleanenv --bind /mnt/your_dir:/mnt/your_dir easyr1.sif
+```
+
+### Hardware Requirements
+
+\* *estimated*
+
+| Method                   | Bits |  1.5B  |   3B   |   7B   |   32B   |   72B   |
+| ------------------------ | ---- | ------ | ------ | ------ | ------- | ------- |
+| GRPO Full Fine-Tuning    |  AMP | 2*24GB | 4*40GB | 8*40GB | 16*80GB | 32*80GB |
+| GRPO Full Fine-Tuning    | BF16 | 1*24GB | 1*40GB | 4*40GB |  8*80GB | 16*80GB |
+
+> [!NOTE]
+> Use `worker.actor.fsdp.torch_dtype=bf16` and `worker.actor.optim.strategy=adamw_bf16` to enable bf16 training.
+>
+> We are working hard to reduce the VRAM in RL training, LoRA support will be integrated in next updates.
+
+## Tutorial: Run Qwen2.5-VL GRPO on [Geometry3K](https://huggingface.co/datasets/hiyouga/geometry3k) Dataset in Just 3 Steps
+
+![image](assets/qwen2_5_vl_7b_geo.png)
+
+### Installation
+
+```bash
+git clone https://github.com/hiyouga/EasyR1.git
+cd EasyR1
+pip install -e .
+```
+
+### GRPO Training
+
+```bash
+bash examples/qwen2_5_vl_7b_geo3k_grpo.sh
+```
+
+### Merge Checkpoint in Hugging Face Format
+
+```bash
+python3 scripts/model_merger.py --local_dir checkpoints/easy_r1/exp_name/global_step_1/actor
+```
+
+> [!TIP]
+> If you encounter issues with connecting to Hugging Face, consider using `export HF_ENDPOINT=https://hf-mirror.com`.
+>
+> If you want to use SwanLab logger, consider using `bash examples/qwen2_5_vl_7b_geo3k_swanlab.sh`.
+
+## Custom Dataset
+
+Please refer to the example datasets to prepare your own dataset.
+
+- Text dataset: https://huggingface.co/datasets/hiyouga/math12k
+- Image-text dataset: https://huggingface.co/datasets/hiyouga/geometry3k
+- Multi-image-text dataset: https://huggingface.co/datasets/hiyouga/journeybench-multi-image-vqa
+- Text-image mixed dataset: https://huggingface.co/datasets/hiyouga/rl-mixed-dataset
+
+## How to Understand GRPO in EasyR1
+
+![image](assets/easyr1_grpo.png)
+
+- To learn about the GRPO algorithm, you can refer to [Hugging Face's blog](https://huggingface.co/docs/trl/v0.16.1/en/grpo_trainer).
+
+## How to Run 70B+ Model in Multi-node Environment
+
+1. Start the Ray head node.
+
+```bash
+ray start --head --port=6379 --dashboard-host=0.0.0.0
+```
+
+2. Start the Ray worker node and connect to the head node.
+
+```bash
+ray start --address=<head_node_ip>:6379
+```
+
+3. Check the Ray resource pool.
+
+```bash
+ray status
+```
+
+4. Run training script on the Ray head node only.
+
+```bash
+bash examples/qwen2_5_vl_7b_geo3k_grpo.sh
+```
+
+See the **[veRL's official doc](https://verl.readthedocs.io/en/latest/start/multinode.html)** for more details about multi-node training and Ray debugger.
+
+## Other Baselines
+
+We also reproduced the following two baselines of the [R1-V](https://github.com/deep-agent/R1-V) project.
+- [CLEVR-70k-Counting](examples/baselines/qwen2_5_vl_3b_clevr.sh): Train the Qwen2.5-VL-3B-Instruct model on counting problem.
+- [GeoQA-8k](examples/baselines/qwen2_5_vl_3b_geoqa8k.sh): Train the Qwen2.5-VL-3B-Instruct model on GeoQA problem.
+
+## Performance Baselines
+
+See [baselines.md](assets/baselines.md).
+
+## Awesome Work using EasyR1
+
+- **MMR1**: Advancing the Frontiers of Multimodal Reasoning. [![[code]](https://img.shields.io/github/stars/LengSicong/MMR1)](https://github.com/LengSicong/MMR1)
+- **Vision-R1**: Incentivizing Reasoning Capability in Multimodal Large Language Models. [![[code]](https://img.shields.io/github/stars/Osilly/Vision-R1)](https://github.com/Osilly/Vision-R1) [![[arxiv]](https://img.shields.io/badge/arxiv-2503.06749-blue)](https://arxiv.org/abs/2503.06749)
+- **Seg-Zero**: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement. [![[code]](https://img.shields.io/github/stars/dvlab-research/Seg-Zero)](https://github.com/dvlab-research/Seg-Zero) [![[arxiv]](https://img.shields.io/badge/arxiv-2503.06520-blue)](https://arxiv.org/abs/2503.06520)
+- **MetaSpatial**: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse. [![[code]](https://img.shields.io/github/stars/PzySeere/MetaSpatial)](https://github.com/PzySeere/MetaSpatial) [![[arxiv]](https://img.shields.io/badge/arxiv-2503.18470-blue)](https://arxiv.org/abs/2503.18470)
+- **Temporal-R1**: Envolving Temporal Reasoning Capability into LMMs via Temporal Consistent Reward. [![[code]](https://img.shields.io/github/stars/appletea233/Temporal-R1)](https://github.com/appletea233/Temporal-R1)
+- **NoisyRollout**: Reinforcing Visual Reasoning with Data Augmentation. [![[code]](https://img.shields.io/github/stars/John-AI-Lab/NoisyRollout)](https://github.com/John-AI-Lab/NoisyRollout) [![[arxiv]](https://img.shields.io/badge/arxiv-2504.13055-blue)](https://arxiv.org/pdf/2504.13055)
+- **GUI-R1**: A Generalist R1-Style Vision-Language Action Model For GUI Agents. [![[code]](https://img.shields.io/github/stars/ritzz-ai/GUI-R1)](https://github.com/ritzz-ai/GUI-R1) [![[arxiv]](https://img.shields.io/badge/arxiv-2504.10458-blue)](https://arxiv.org/abs/2504.10458)
+- **R1-Track**: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning. [![[code]](https://img.shields.io/github/stars/Wangbiao2/R1-Track)](https://github.com/Wangbiao2/R1-Track)
+- **VisionReasoner**: Unified Visual Perception and Reasoning via Reinforcement Learning. [![[code]](https://img.shields.io/github/stars/dvlab-research/VisionReasoner)](https://github.com/dvlab-research/VisionReasoner) [![[arxiv]](https://img.shields.io/badge/arxiv-2505.12081-blue)](https://arxiv.org/abs/2505.12081)
+- **MM-UPT**: Unsupervised Post-Training for Multi-Modal LLM Reasoning via GRPO. [![[code]](https://img.shields.io/github/stars/waltonfuture/MM-UPT)](https://github.com/waltonfuture/MM-UPT) [![[arxiv]](https://img.shields.io/badge/arxiv-2505.22453-blue)](https://arxiv.org/pdf/2505.22453)
+- **RL-with-Cold-Start**: Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start. [![[code]](https://img.shields.io/github/stars/waltonfuture/RL-with-Cold-Start)](https://github.com/waltonfuture/RL-with-Cold-Start) [![[arxiv]](https://img.shields.io/badge/arxiv-2505.22334-blue)](https://arxiv.org/pdf/2505.22334)
+- **ViGoRL**: Grounded Reinforcement Learning for Visual Reasoning. [![[code]](https://img.shields.io/github/stars/Gabesarch/grounded-rl)](https://github.com/Gabesarch/grounded-rl) [![[arxiv]](https://img.shields.io/badge/arxiv-2505.22334-blue)](https://arxiv.org/abs/2505.23678)
+- **Revisual-R1**: Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning. [![[code]](https://img.shields.io/github/stars/CSfufu/Revisual-R1)](https://github.com/CSfufu/Revisual-R1) [![[arxiv]](https://img.shields.io/badge/arxiv-2506.04207-blue)](https://arxiv.org/abs/2506.04207)
+- **SophiaVL-R1**: Reinforcing MLLMs Reasoning with Thinking Reward. [![[code]](https://img.shields.io/github/stars/kxfan2002/SophiaVL-R1)](https://github.com/kxfan2002/SophiaVL-R1) [![[arxiv]](https://img.shields.io/badge/arxiv-2505.17018-blue)](https://arxiv.org/abs/2505.17018)
+- **Vision-Matters**: Simple Visual Perturbations Can Boost Multimodal Math Reasoning. [![[code]](https://img.shields.io/github/stars/YutingLi0606/Vision-Matters)](https://github.com/YutingLi0606/Vision-Matters) [![[arxiv]](https://img.shields.io/badge/arxiv-2506.09736-blue)](https://arxiv.org/abs/2506.09736)
+- **VTool-R1**: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use. [![[code]](https://img.shields.io/github/stars/VTOOL-R1/vtool-r1)](https://github.com/VTOOL-R1/vtool-r1) [![[arxiv]](https://img.shields.io/badge/arxiv-2505.19255-blue)](https://arxiv.org/abs/2505.19255)
+- **Long-RL**: Scaling RL to Long Sequences. [![[code]](https://img.shields.io/github/stars/NVlabs/Long-RL)](https://github.com/NVlabs/Long-RL) [![[arxiv]](https://img.shields.io/badge/arxiv-2507.07966-blue)](https://arxiv.org/abs/2507.07966)
+
+## TODO
+
+- Support LoRA (high priority).
+- Support ulysses parallelism for VLMs (middle priority).
+- Support more VLM architectures.
+
+> [!NOTE]
+> We will not provide scripts for supervised fine-tuning and inference in this project. If you have such requirements, we recommend using [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory).
+
+### Known bugs
+
+These features are temporarily disabled for now, we plan to fix them one-by-one in the future updates.
+
+- Vision language models are not compatible with ulysses parallelism yet.
+
+## Discussion Group
+
+👋 Join our [WeChat group](assets/wechat.jpg).
+
+## FAQs
+
+> ValueError: Image features and image tokens do not match: tokens: 8192, features 9800
+
+Increase the `data.max_prompt_length` or reduce the `data.max_pixels`.
+
+> RuntimeError: CUDA Error: out of memory at /workspace/csrc/cumem_allocator.cpp:62
+
+Reduce the `worker.rollout.gpu_memory_utilization` and enable `worker.actor.offload.offload_params`.
+
+> RuntimeError: 0 active drivers ([]). There should only be one.
+
+Uninstall `deepspeed` from the current python environment.
+
+## Citation
+
+Core contributors: [Yaowei Zheng](https://github.com/hiyouga), [Junting Lu](https://github.com/AL-377), [Shenzhi Wang](https://github.com/Shenzhi-Wang), [Zhangchi Feng](https://github.com/BUAADreamer), [Dongdong Kuang](https://github.com/Kuangdd01) and Yuwen Xiong
+
+We also thank Guangming Sheng and Chi Zhang for helpful discussions.
+
+```bibtex
+@misc{zheng2025easyr1,
+  title        = {EasyR1: An Efficient, Scalable, Multi-Modality RL Training Framework},
+  author       = {Yaowei Zheng, Junting Lu, Shenzhi Wang, Zhangchi Feng, Dongdong Kuang, Yuwen Xiong},
+  howpublished = {\url{https://github.com/hiyouga/EasyR1}},
+  year         = {2025}
+}
+```
+
+We recommend to also cite the original work.
+
+```bibtex
+@article{sheng2024hybridflow,
+  title   = {HybridFlow: A Flexible and Efficient RLHF Framework},
+  author  = {Guangming Sheng and Chi Zhang and Zilingfeng Ye and Xibin Wu and Wang Zhang and Ru Zhang and Yanghua Peng and Haibin Lin and Chuan Wu},
+  year    = {2024},
+  journal = {arXiv preprint arXiv: 2409.19256}
+}
+```