Add pipeline tag, library name, and GitHub README content (#1)
Browse files- Add pipeline tag, library name, and GitHub README content (4938dbbb2a27149c40529a6365e3f3eadd121400)
Co-authored-by: Niels Rogge <[email protected]>
README.md
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---
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datasets:
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- WaltonFuture/Multimodal-Cold-Start
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- WaltonFuture/Multimodal-RL-Data
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---
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* 🐙 **GitHub Repo:** [waltonfuture/RL-with-Cold-Start](https://github.com/waltonfuture/RL-with-Cold-Start)
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* 📜 **Paper (arXiv):** [Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start (arXiv:2505.22334)](https://arxiv.org/abs/2505.22334)
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---
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base_model:
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- Qwen/Qwen2.5-VL-7B-Instruct
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datasets:
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- WaltonFuture/Multimodal-Cold-Start
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- WaltonFuture/Multimodal-RL-Data
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license: apache-2.0
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pipeline_tag: image-text-to-text
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library_name: transformers
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---
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* 🐙 **GitHub Repo:** [waltonfuture/RL-with-Cold-Start](https://github.com/waltonfuture/RL-with-Cold-Start)
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* 📜 **Paper (arXiv):** [Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start (arXiv:2505.22334)](https://arxiv.org/abs/2505.22334)
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<div align=center>
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<img src="assets/model_comparison.png" width = "80%" alt="model_comparison" align=center/>
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</div>
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## Cold Start Stage
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We conduct supervised fine-tuning on Qwen2.5-VL-3B and Qwen2.5-VL-7B using [ms-swift](https://github.com/modelscope/ms-swift). In this stage, please refer to this curated [dataset](https://huggingface.co/datasets/WaltonFuture/Multimodal-Cold-Start) distilled from Qwen2.5-VL-32B using rejection sampling.
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### Setup
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```bash
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git clone https://github.com/waltonfuture/RL-with-Cold-Start.git
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cd RL-with-Cold-Start/SFT
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pip install -e .
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```
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### Prepare Data
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```bash
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python convert_data.py
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```
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### SFT
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```bash
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bash qwen2.5vl_sft.sh
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```
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The checkpoint can be found in SFT/output.
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## RL Stage
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We further conduct GRPO using [EasyR1](https://github.com/hiyouga/EasyR1). Please refer to this [dataset](https://huggingface.co/datasets/WaltonFuture/Multimodal-RL-Data) for the GRPO training.
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### Setup
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```bash
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git clone https://github.com/waltonfuture/RL-with-Cold-Start.git
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cd RL-with-Cold-Start/GRPO
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pip install -e .
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```
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### GRPO Training (replace the checkpoint with the model after SFT)
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```bash
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bash examples/qwen2_5_vl_7b_grpo.sh
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```
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### Merge Checkpoint in Hugging Face Format
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```bash
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python3 scripts/model_merger.py --local_dir checkpoints/easyr1/qwen2_5_vl_7b_grpo/global_step_80/actor
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```
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## Data Access
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Our two stage datasets are now available on Huggingface.
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| Stage | Data |
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| ------------------ | ------------- |
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| Cold Start | [Multimodal-Cold-Start](https://huggingface.co/datasets/WaltonFuture/Multimodal-Cold-Start) |
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| RL | [Multimodal-RL-Data](https://huggingface.co/datasets/WaltonFuture/Multimodal-RL-Data) |
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## Model Access
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Our models are now available on Huggingface.
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| Backbone | Our model |
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| ------------------ | ------------- |
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| Qwen2.5-VL-7b | [Qwen2.5VL-7b-RL-with-Cold-Start](https://huggingface.co/WaltonFuture/Qwen2.5VL-7b-RLCS) |
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| Qwen2.5-VL-3b | [Qwen2.5VL-3b-RL-with-Cold-Start](https://huggingface.co/WaltonFuture/Qwen2.5VL-3b-RLCS) |
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## Acknowledgment
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Our models are built upon the amazing [Qwen2.5-VL](https://huggingface.co/collections/Qwen/qwen25-vl-6795ffac22b334a837c0f9a5) family.
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We thank [EasyR1](https://github.com/hiyouga/EasyR1) and [ms-swift](https://github.com/modelscope/ms-swift) for their training codes.
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## Contact
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Please contact Lai Wei ([email protected]) if needed.
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## Citation
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```
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@article{wei2025advancing,
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title={Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start},
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author={Wei, Lai and Li, Yuting and Zheng, Kaipeng and Wang, Chen and Wang, Yue and Kong, Linghe and Sun, Lichao and Huang, Weiran},
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journal={arXiv preprint arXiv:2505.22334},
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year={2025}
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}
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```
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