Datasets:

Yuting6
/

ttrl

@@ -1,170 +1,71 @@
-# 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)
-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 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](#custom-dataset)
-- 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](./Dockerfile) to easily build environments.
-We recommend using the [pre-built docker image](https://hub.docker.com/r/hiyouga/verl) in EasyR1.
-```bash
-# 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 |
-> [!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
-- Vision-text dataset: https://huggingface.co/datasets/hiyouga/geometry3k
-> [!TIP]
-> EasyR1 already supports multi-image 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/v0.15.2/en/grpo_trainer).
-## How to Run 70B+ Model in Multi-node Environment
-Please see the **[veRL's official doc](https://verl.readthedocs.io/en/latest/start/multinode.html)** for 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.
-## 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)
-## 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).
 ## 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}
-}
-```

+---
+task_categories:
+- image-text-to-text
+tags:
+- multimodal
+- reasoning
+- chain-of-thought
+- vision-r1
+---
+# Vision-R1-cold
+Vision-R1-cold is a high-quality multimodal Chain-of-Thought (CoT) dataset consisting of 200,000 samples. It serves as the cold-start initialization data for **Vision-R1**, a multimodal large language model (MLLM) designed to enhance reasoning capabilities through Reinforcement Learning (RL).
+- **Paper:** [Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models](https://huggingface.co/papers/2503.06749)
+- **Repository:** [https://github.com/Osilly/Vision-R1](https://github.com/Osilly/Vision-R1)
+## Dataset Description
+The dataset was constructed without human annotations by leveraging an existing MLLM and DeepSeek-R1. The authors used a **Modality Bridging** approach where multimodal data is first processed by an MLLM to obtain a "Pseudo-CoT" (caption and reasoning process). This textual description is then fed to DeepSeek-R1 to facilitate the generation of high-quality, complex CoT processes. The resulting data is post-processed and filtered to create the final 200K dataset.
+## Usage
+To use this dataset with the [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) framework, you can update your `dataset_info.json` with the following configuration:
+```json
+"vision_r1_llava_cot_full": {
+    "file_name": "vision_r1_llava_cot_full.json",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "images": "image"
+    },
+    "tags": {
+      "role_tag": "from",
+      "content_tag": "value",
+      "user_tag": "human",
+      "assistant_tag": "gpt"
+    }
+  },
+  "vision_r1_mulberry_sft_full": {
+    "file_name": "vision_r1_mulberry_sft_full.json",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "conversations",
+      "images": "images"
+    },
+    "tags": {
+      "role_tag": "from",
+      "content_tag": "value",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  }
 ```
+**Note:** The images for this dataset are derived from [LLaVA-CoT-100k](https://huggingface.co/datasets/Xkev/LLaVA-CoT-100k) and [Mulberry-SFT](https://huggingface.co/datasets/HuanjinYao/Mulberry-SFT).
 ## Citation
 ```bibtex
+@misc{huang2025visionr1incentivizingreasoningcapability,
+      title={Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models},
+      author={Wenxuan Huang and Bohan Jia and Zijie Zhai and Shaosheng Cao and Zheyu Ye and Fei Zhao and Yao Hu and Shaohui Lin},
+      year={2025},
+      eprint={2503.06749},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2503.06749},
 }
+```