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R1-V

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Witness the aha moment of VLM with less than $3.

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# R1-V: Reinforcing Super Generalization Ability in Vision Language Models with Less Than $3

News: We released new VLM-RL environments, training codebase and research paper [G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning](https://github.com/chenllliang/G1), check it out!

![image](https://github.com/user-attachments/assets/c52a448f-d666-4ca6-958b-86267d56de0e)

> ### Roadmap for R1-V
> We are building a general framework for RLVR in VLM. We believe in the power of **trenches** and **longtermism**.
>
> Our Interest: General Vision-Language Intelligence & Visual/GUI Agent
> 
> Our Goal: 🔄 Algorithm Enhancement ⚡ Efficiency Optimization 🎯 Task Diversity 🌲 Impactful Open Source Research. 
>
> Welcome Ideas and Contribution. Stay tuned!

**Blogs:**

[🎯 RLVR in Vision Language Models: Findings, Questions and Directions](https://deepagent.notion.site/rlvr-in-vlms)

**Resources:**

[🤗 R1V Training Dataset: CLEVR-70k-Counting](https://huggingface.co/datasets/leonardPKU/clevr_cogen_a_train)

[🤗 R1V Training Dataset: CLEVR-70k-Complex](https://huggingface.co/datasets/MMInstruction/Clevr_CoGenT_TrainA_70K_Complex)

[🤗 R1V Training Dataset: GEOQA-8k](https://huggingface.co/datasets/leonardPKU/GEOQA_R1V_Train_8K)

[🤗 R1-Distilled Visual Reasoning Dataset](https://huggingface.co/datasets/MMInstruction/Clevr_CoGenT_TrainA_R1)

**R1-V Team:**

[Liang Chen](https://github.com/chenllliang) · [Lei Li](https://lilei-nlp.github.io) · [Haozhe Zhao](https://haozhezhao.github.io/) · [Yifan Song](https://github.com/Yifan-Song793) · [Vinci](https://github.com/0xvincii) · [Zihao Yue](https://yuezih.github.io/)

**Contributors**:

---

### Updates

- 2025-02-27: vLLM trainer supports Qwen2.5-VL now, refer to `./src/scripts/run_grpo_vllm_qwen25vl.sh` for script and env update.
- 2025-02-21: We write a [blog post](https://deepagent.notion.site/rlvr-in-vlms) summarizing the main findings and questions in our visual RLVR experimetns, check it out!
- 2025-02-12: We fixed the batched decoding error. The orignial RL training scirpt now is 3x speeded up.
- 2025-02-12: R1-V now supports vLLM to accelerate training (`pip install vllm==0.7.2` before use) and SFT.
- 2025-02-11: R1-V now supports Qwen2.5-VL and [GEOQA](https://arxiv.org/abs/2312.11370) task.
- 2025-02-06: We upload the evaluation script and polish the README. We are writing a blog post summarizing the statistics, findings and underexplored questions. 
- 2025-02-03: We upload the training codebase.
- 2025-02-03: We curate and upload some verified Deepseek-R1 visual reasoning traces with some special tricks (see `R1-V/src/distill_r1/`). Current training code does not rely on it, feel free to explore.
- 2025-02-03: We release the R1-V repo.

### For contributors
- Our top development priority is addressing the issues marked with `help wanted` labels, and we welcome ideas/PRs from the community to help solve them.

---

![Image](https://github.com/user-attachments/assets/e86a3ff2-a9c6-4548-8200-6c3c382d60e6)

![Image](https://github.com/user-attachments/assets/b3512920-ef30-4d6d-9bfe-c64e4570a067)
*Note: In our later experiment, we found that letting the 2b base model directly output the result instead of following `<think></think><answer></answer>` would lead to a much higher score (86%) on SuperClevr. It suggests that enforcing Chain-of-Thought reasoning may be not only unnecessary but potentially detrimental to the 2B model performance.*

![image](https://github.com/user-attachments/assets/f5191b1e-dde2-42b7-9ec9-10f7f6213c12)

## Setup

```bash
conda create -n r1-v python=3.11 
conda activate r1-v

bash setup.sh
```

> [!NOTE] 
> If you meet bug when running the script, first try align your environments with `./src/requirements.txt`

### Supported Models

1. Qwen2-VL
2. Qwen2.5-VL

### Supported Training Datasets

1. [🤗 R1V Training Dataset: CLEVR-70k-Counting](https://huggingface.co/datasets/leonardPKU/clevr_cogen_a_train): Item Counting Problems

2. [🤗 R1V Training Dataset: CLEVR-70k-Complex](https://huggingface.co/datasets/MMInstruction/Clevr_CoGenT_TrainA_70K_Complex): Number Related Reasoning

3. [🤗 R1V Training Dataset: GEOQA-8k](https://huggingface.co/datasets/leonardPKU/GEOQA_R1V_Train_8K): Geometry Reasoning

### Supported Evaluations

1. [SuperClevr-200](https://github.com/Deep-Agent/R1-V?tab=readme-ov-file#superclevr): Item Counting Problems
2. [GeoQA-Test-Direct-Answer-735](https://github.com/Deep-Agent/R1-V?tab=readme-ov-file#geoqa): Geometry Reasoning

## Training

### GRPO

```bash
cd src/r1-v

export DEBUG_MODE="true" # Enable Debug if you want to see the rollout of model during RL
export LOG_PATH="./debug_log_2b.txt"

torchrun --nproc_per_node="8" \
    --nnodes="1" \
    --node_rank="0" \
    --master_addr="127.0.0.1" \
    --master_port="12345" \
    src/open_r1/grpo.py \
    --output_dir <OUTPUT_DIR> \
    --model_name_or_path <PATH-TO-Qwen2-VL-2B-Instruct> \ 
    --dataset_name leonardPKU/clevr_cogen_a_train \  
    --deepspeed local_scripts/zero3.json \
    --max_prompt_length 512 \
    --max_completion_length 512 \
    --per_device_train_batch_size 1 \
    --gradient_accumulation_steps 2 \
    --logging_steps 1 \
    --bf16 \
    --report_to wandb \
    --gradient_checkpointing false \
    --attn_implementation flash_attention_2 \
    --max_pixels 401408 \
    --num_train_epochs 2 \
    --run_name Qwen2-VL-2B-GRPO-CLEVR-70k \
    --save_steps 100 \
    --save_only_model true \
    --num_generations 8   # number of outputs G in grpo, reduce it would lead to faster training and smaller memory cost but higher variance

```

> [!NOTE] 
> 1. To reproduce the result, keep the per_device_train_batch_size to 1 for now, as there is a revealed bug about batched training. See the [reproduction report](https://github.com/Deep-Agent/R1-V/issues/4#issuecomment-2633348354) here. We realize it is important for effiency and are working on solving it with the community.
> 2. If you meet **OOM Error**, you can try reduce `--num_generations`
> 3. To use vLLM to speed up, please refer to this [script](https://github.com/Deep-Agent/R1-V/blob/main/src/scripts/run_grpo_vllm.sh).

### SFT

We also provide SFT code, please follow the script and edit the config to customize the sft task.

```bash
accelerate launch --config_file src/r1-v/configs/zero2.yaml src/r1-v/src/open_r1/sft.py --config src/r1-v/configs/qwen2vl_sft_config.yaml 
```

## Evaluation

### SuperCLEVR

![image](https://github.com/user-attachments/assets/4f48233c-0546-432f-94e6-723f91fbd086)

We provide the example script to evaluate OOD counting performance on a subset of SuperCLEVR within 1 minute. You can also modify the script and dataset to test on your own dataset.

```bash
cd ./src/eval
wget https://www.cs.jhu.edu/~zhuowan/zhuowan/SuperCLEVR/to_be_released/images.zip
unzip images.zip

# change the model path in the script
python test_qwen2vl_counting_superclevr.py

# tested scores: 
# Qwen2VL-2B-Instruct: 48.0%
# Qwen2VL-2B-Instruct-GRPO-100step: 82.5%
```

### GEOQA

We provide the example script to evaluate on the test set (direct answer form) of [GEOQA](https://arxiv.org/abs/2312.11370).

```bash
# prepare images for testing
cd ./src/eval
git lfs install
git clone https://huggingface.co/datasets/Luckyjhg/Geo170K
cd Geo170K
unzip images.zip

# Evaluation Script
python test_qwen2vl_geoqa.py

# tested scores: 
# Qwen2VL-7B-Instruct: 30.63%
# Qwen2VL-7B-Instruct-GRPO-2epochs: 38.72%

# Qwen2.5VL-3B-Instruct: 35.41%
# Qwen2.5VL-3B-Instruct-GRPO-1epochs: 47.48%
```

To enable faster inference with multiple GPUs, you could also use the script in `R1-V/src/scripts/test_grpo_geoqa_multigpu.sh`
```
bash src/scripts/test_grpo_geoqa_multigpu.sh
```

## Acknowledgements

We sincerely thank [DeepSeek](https://github.com/deepseek-ai/DeepSeek-R1), [Open-R1](https://github.com/huggingface/open-r1), [QwenVL](https://github.com/QwenLM/Qwen2.5-VL), [Open-R1-Multimodal](https://github.com/EvolvingLMMs-Lab/open-r1-multimodal) (our initial codebase), [CLEVR](https://cs.stanford.edu/people/jcjohns/clevr/), [SuperCLEVR](https://github.com/Lizw14/Super-CLEVR), [G-LLAVA](https://arxiv.org/abs/2312.11370) for providing open source resources and to build the project. Special thanks to [Kimi](https://kimi.moonshot.cn/), [bAInance Labs](https://bainancelabs.com/) for supporting computation resources and [Yuxin Wu](https://scholar.google.com/citations?user=mJQI-gUAAAAJ&hl=en), [Xinyu Zhou](https://scholar.google.com/citations?user=Jv4LCj8AAAAJ&hl=en), [Baobao Chang](https://scholar.google.com.au/citations?user=LaKNyhQAAAAJ&hl=en) for their valuable advice.

[![Star History Chart](https://api.star-history.com/svg?repos=Deep-Agent/R1-V&type=Timeline)](https://star-history.com/#Deep-Agent/R1-V&Timeline)

## Citation

```bib
@misc{chen2025r1v,
  author       = {Chen, Liang and Li, Lei and Zhao, Haozhe and Song, Yifan and Vinci},
  title        = {R1-V: Reinforcing Super Generalization Ability in Vision-Language Models with Less Than \$3},
  howpublished = {\url{https://github.com/Deep-Agent/R1-V}},
  note         = {Accessed: 2025-02-02},
  year         = {2025}
}
```

R1-V

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