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

Used by Amazon Web Services

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/Qwen3 language models
  • Qwen2-VL/Qwen2.5-VL/Qwen3-VL vision language models
  • DeepSeek-R1 distill models

• Supported algorithms

  • GRPO
  • DAPO
  • Reinforce++
  • ReMax
  • RLOO
  • GSPO
  • CISPO

• Supported datasets

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

• Supported tricks

  • Padding-free training
  • LoRA training
  • Resuming from the latest/best checkpoint
  • Wandb & SwanLab & Mlflow & Tensorboard tracking

Requirements

Software Requirements

• Python 3.9+
• transformers>=4.54.0
• flash-attn>=2.4.3
• vllm>=0.8.3

We provide a Dockerfile to easily build environments.

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

docker pull hiyouga/verl:ngc-th2.8.0-cu12.9-vllm0.11.0
docker run -it --ipc=host --gpus=all hiyouga/verl:ngc-th2.8.0-cu12.9-vllm0.11.0

If your environment does not support Docker, you can consider using Apptainer:

apptainer pull easyr1.sif docker://hiyouga/verl:ngc-th2.8.0-cu12.9-vllm0.11.0
apptainer shell --nv --cleanenv --bind /mnt/your_dir:/mnt/your_dir easyr1.sif

Use USE_MODELSCOPE_HUB=1 to download models from the ModelScope hub.

Hardware Requirements

* estimated

| Method | Bits | 1.5B | 3B | 7B | 32B | 72B | | ------------------------ | ---- | ------ | ------ | ------ | ------- | ------- | | GRPO Full Fine-Tuning | AMP | 224GB | 440GB | 840GB | 1680GB | 3280GB | | GRPO Full Fine-Tuning | BF16 | 124GB | 140GB | 440GB | 880GB | 1680GB | | GRPO LoRA Fine-Tuning | AMP | 112GB | 124GB | 232GB | 280GB | 4*80GB |

[!NOTE] Use worker.actor.fsdp.torch_dtype=bf16 and worker.actor.optim.strategy=adamw_bf16 to enable bf16 training.

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 Full Training

bash examples/qwen2_5_vl_7b_geo3k_grpo.sh

GRPO LoRA Training

bash examples/qwen3_vl_4b_geo3k_grpo_lora.sh

Merge Checkpoint in Hugging Face Format

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

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

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

1. Start the Ray head node.

ray start --head --port=6379 --dashboard-host=0.0.0.0

2. Start the Ray worker node and connect to the head node.

ray start --address=<head_node_ip>:6379

3. Check the Ray resource pool.

ray status

4. Run training script on the Ray head node only.

bash examples/qwen2_5_vl_7b_geo3k_grpo.sh

See the veRL's official doc for more details about 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.

Performance Baselines

See baselines.md.

Awesome Work using EasyR1

• MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resources.
• Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models.
• Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement.
• MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse.
• Temporal-R1: Envolving Temporal Reasoning Capability into LMMs via Temporal Consistent Reward.
• NoisyRollout: Reinforcing Visual Reasoning with Data Augmentation.
• GUI-R1: A Generalist R1-Style Vision-Language Action Model For GUI Agents.
• FAST-GRPO: Fast-Slow Thinking framework that dynamically adapts reasoning depth based on question characteristics.
• R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning.
• VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement Learning.
• MM-UPT: Unsupervised Post-Training for Multi-Modal LLM Reasoning via GRPO.
• RL-with-Cold-Start: Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start.
• ViGoRL: Grounded Reinforcement Learning for Visual Reasoning.
• Revisual-R1: Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning.
• SophiaVL-R1: Reinforcing MLLMs Reasoning with Thinking Reward.
• Vision-Matters: Simple Visual Perturbations Can Boost Multimodal Math Reasoning. [![[code]](https://img.shield