Yuting6/ttrl

EasyR1: An Efficient, Scalable, Multi-Modality RL Training Framework

This project is a clean fork of the original 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 and the latest release of vLLM's SPMD mode.

Features

• Supported models

  • Llama3/Qwen2/Qwen2.5 language models
  • Qwen2/Qwen2.5-VL vision language models
  • DeepSeek-R1 distill models

• Supported algorithms

  • GRPO
  • Reinforce++
  • ReMax
  • RLOO

• Supported datasets

  • Any text, vision-text dataset in a specific format

• Supported tricks

  • Padding-free training
  • Resuming from checkpoint
  • Wandb & SwanLab & Mlflow & Tensorboard tracking

Requirements

Software Requirements

• Python 3.9+
• transformers>=4.49.0
• flash-attn>=2.4.3
• vllm>=0.7.3

We provide a Dockerfile to easily build environments.

We recommend using the pre-built docker image in EasyR1.

# stable
docker pull hiyouga/verl:ngc-th2.5.1-cu120-vllm0.7.4-hotfix
# nightly
docker pull hiyouga/verl:ngc-th2.6.0-cu120-vllm0.8.2

Hardware Requirements

* estimated

Method	Bits	1.5B	3B	7B	32B
GRPO Full Fine-Tuning	AMP	2*24GB	4*40GB	8*40GB	16*80GB
GRPO Full Fine-Tuning	BF16	1*24GB	1*40GB	4*40GB	8*80GB

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 Dataset in Just 3 Steps

Installation

git clone https://github.com/hiyouga/EasyR1.git
cd EasyR1
pip install -e .

GRPO Training

bash examples/qwen2_5_vl_7b_geo3k_grpo.sh

Merge Checkpoint in Hugging Face Format

python3 scripts/model_merger.py --local_dir checkpoints/easy_r1/exp_name/global_step_1/actor

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
• Vision-text dataset: https://huggingface.co/datasets/hiyouga/geometry3k

EasyR1 already supports multi-image dataset.

How to Understand GRPO in EasyR1

• To learn about the GRPO algorithm, you can refer to Hugging Face's blog.

How to Run 70B+ Model in Multi-node Environment

Please see the veRL's official doc for multi-node training and Ray debugger.

Other Baselines

We also reproduced the following two baselines of the R1-V project.

• CLEVR-70k-Counting: Train the Qwen2.5-VL-3B-Instruct model on counting problem.
• GeoQA-8k: Train the Qwen2.5-VL-3B-Instruct model on GeoQA problem.

Awesome Work using EasyR1

• MMR1: Advancing the Frontiers of Multimodal Reasoning. [](https://github.com/LengSicong/MMR1)
• Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models. [](https://github.com/Osilly/Vision-R1) [](https://arxiv.org/abs/2503.06749)
• Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement. [](https://github.com/dvlab-research/Seg-Zero) [](https://arxiv.org/abs/2503.06520)
• MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse. [](https://github.com/PzySeere/MetaSpatial) [](https://arxiv.org/abs/2503.18470)

TODO

• Support LoRA (high priority).
• Support ulysses parallelism for VLMs (middle priority).
• Support more VLM architectures.

We will not provide scripts for supervised fine-tuning and inference in this project. If you have such requirements, we recommend using 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.

Citation

Core contributors: Yaowei Zheng, Junting Lu, Shenzhi Wang, Zhangchi Feng, Dongdong Kuang and Yuwen Xiong

We also thank Guangming Sheng and Chi Zhang for helpful discussions.

@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.

@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}
}