Improve model card: update pipeline tag, add library name, paper details & content

README.md

@@ -1,12 +1,156 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
 datasets:
 - QiWang98/VideoRFT-Data
 language:
 - en
+license: apache-2.0
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen2.5-VL-7B-Instruct
-pipeline_tag: visual-question-answering
----
+pipeline_tag: video-text-to-text
+library_name: transformers
+---
+
+# 🎥 $\text{VideoRFT}$: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning
+
+📑 [Paper: VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning](https://huggingface.co/papers/2505.12434)
+⭐️ [Code: https://github.com/QiWang98/VideoRFT](https://github.com/QiWang98/VideoRFT)
+📀 [CoT Dataset: https://huggingface.co/datasets/QiWang98/VideoRFT-Data](https://huggingface.co/datasets/QiWang98/VideoRFT-Data)
+📀 [RL Dataset: https://huggingface.co/datasets/QiWang98/VideoRFT-Data](https://huggingface.co/datasets/QiWang98/VideoRFT-Data)
+🤗 [Models: https://huggingface.co/QiWang98/VideoRFT](https://huggingface.co/QiWang98/VideoRFT)
+
+## 📰 News
+- [2025/09/19] Our paper has been **accepted to NeurIPS 2025** 🎉! 
+- [2025/06/01] We released our 3B Models ([🤗VideoRFT-SFT-3B](https://huggingface.co/QiWang98/VideoRFT-SFT-3B) and [🤗VideoRFT-3B](https://huggingface.co/QiWang98/VideoRFT-3B)) to huggingface.
+- [2025/05/25] We released our 7B Models ([🤗VideoRFT-SFT-7B](https://huggingface.co/QiWang98/VideoRFT-SFT) and [🤗VideoRFT-7B](https://huggingface.co/QiWang98/VideoRFT)) to huggingface.
+- [2025/05/20] We released our Datasets ([📀CoT Dataset](https://huggingface.co/datasets/QiWang98/VideoRFT-Data) and [📀RL Dataset](https://huggingface.co/datasets/QiWang98/VideoRFT-Data)) to huggingface.
+- [2025/05/18] Our paper is released on [ArXiv](https://arxiv.org/abs/2505.12434), and we have open-sourced our code on [GitHub](https://github.com/QiWang98/VideoRFT)!
+
+## 🔎 Overview
+
+Reinforcement fine-tuning (RFT) has shown great promise in achieving humanlevel reasoning capabilities of Large Language Models (LLMs), and has recently been extended to MLLMs. Nevertheless, reasoning about videos, which is a fundamental aspect of human intelligence, remains a persistent challenge due to the complex logic, temporal and causal structures inherent in video data. To fill this gap, we propose $\textbf{VideoRFT}$, a novel approach that extends the RFT paradigm to cultivate human-like video reasoning capabilities in MLLMs. $\textbf{VideoRFT}$ follows the standard two-stage scheme in RFT: supervised fine-tuning (SFT) with chain-of-thought (CoT) annotations, followed by reinforcement learning (RL) to improve generalization. A central challenge to achieve this in the video domain lies in the scarcity of large-scale, high-quality video CoT datasets. We address this by building a multi-expert-driven, cognition-inspired CoT curation pipeline. First, we devise a cognition-inspired prompting strategy to elicit a reasoning LLM to generate preliminary CoTs based solely on rich, structured, and literal representations of video content. Subsequently, these CoTs are revised by a MLLM conditioned on the actual video, ensuring visual consistency and reducing visual hallucinations. This pipeline results in two new datasets $-$ VideoRFT-CoT-102K for SFT and VideoRFT-RL-310K for RL. To further strengthen the RL phase, we introduce a novel semantic-consistency reward that explicitly promotes the alignment between textual reasoning and visual evidence. This reward encourages the model to produce coherent, context-aware reasoning outputs grounded in visual input. Extensive experiments show that $\textbf{VideoRFT}$ achieves state-of-the-art performance on six video reasoning benchmarks.
+
+<div align="center">
+<img src="https://github.com/QiWang98/VideoRFT/raw/main/images/overview.png" />
+</div>
+
+## ✨ Methodology
+
+To overcome the scarcity of video CoTs, we develop a scalable, cognitively inspired pipeline for high-quality video CoT dataset construction.
+
+<div align="center">
+<img src="https://github.com/QiWang98/VideoRFT/raw/main/images/pipeline.png"  width="95%" />
+</div>
+
+To further strength the RL phase, we introduce a novel semantic-consistency reward that explicitly promotes the alignment between textual reasoning with visual evidence. 
+
+<div align="center">
+<img src="https://github.com/QiWang98/VideoRFT/raw/main/images/grpo.png"  width="95%" />
+</div>
+
+## 📀 Datasets
+
+Based on above pipeline, we construct two large-scale datasets, i.e., [📀VideoRFT-CoT-102K](https://huggingface.co/datasets/QiWang98/VideoRFT-Data) and [📀VideoRFT-RL-310K](https://huggingface.co/datasets/QiWang98/VideoRFT-Data).
+<div align="center">
+<img src="https://github.com/QiWang98/VideoRFT/raw/main/images/dataset.png"  width="50%" />
+</div>
+
+## 🛠️ Set up
+
+### Requirements
+* `Python >= 3.11`
+* `Pytorch >= 2.5.1`
+* `transformers == 4.51.3`
+* `vLLM == 0.7.3`
+* `trl == 0.16.0`
+
+### Installation
+```bash
+git clone https://github.com/QiWang98/VideoRFT
+cd VideoRFT
+
+# Create and activate environment
+conda create -n VideoRFT python=3.11 
+conda activate VideoRFT
+bash setup.sh
+
+# Install decord for improved video processing
+cd src/qwen-vl-utils
+pip install -e .[decord]
+```
+
+## 🚀 Training
+
+### Supervised Fine-Tuning (SFT)
+We begin with supervised fine-tuning on the VideoRFT-CoT dataset for one epoch:
+
+```bash
+bash ./src/scripts/run_sft_video.sh
+```
+
+This step can be skipped by directly using our pretrained SFT models, available at [🤗VideoRFT-SFT-7B](https://huggingface.co/QiWang98/VideoRFT-SFT) or [🤗VideoRFT-SFT-3B](https://huggingface.co/QiWang98/VideoRFT-SFT-3B).
+
+### Reinforcement Learning (RL)
+
+Next, perform reinforcement learning using the VideoRFT-RL dataset:
+
+```bash
+bash ./src/scripts/run_grpo_video.sh
+```
+
+To enable faster training via vLLM acceleration:
+
+```bash
+bash ./src/scripts/run_grpo_vllm_qwen25vl.sh
+```
+
+> **Note:** During training, we adopt the following settings for efficiency:
+
+* **VIDEO PIXELS**: 128 × 28 × 28
+* **FPS FRAMES**: 16
+
+All frame-related configurations can be adjusted in `src/qwen-vl-utils`.
+
+## 📈 Inference & Evaluation
+
+> During inference, we increase the maximum frame resolution and length to boost performance:
+
+* **VIDEO PIXELS**: 256 × 28 × 28
+* **FPS FRAMES**: 32
+
+You can configure these parameters in `src/qwen-vl-utils`.
+
+> We evaluate all models under a unified decoding configuration following the official Qwen2.5-VL demo:
+
+* `top_p = 0.001`
+* `temperature = 0.01`
+
+### Evaluation Procedure
+
+1. Download preprocessed evaluation JSONs from: \[[🤗 eval](https://huggingface.co/datasets/Video-R1/Video-R1-eval)]
+
+2. Download the video data from the official sites of each benchmark and organize them as specified in the JSON files.
+
+3. Run the evaluation across all benchmarks:
+
+```bash
+bash ./src/eval_bench.sh
+```
+
+## 🙏 Acknowledgements
+
+We gratefully acknowledge the contributions of the open-source community, particularly [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1), [Open-R1](https://github.com/huggingface/open-r1), and [R1-V](https://github.com/Deep-Agent/R1-V).
+
+## 📚 Citations
+
+If you find this work helpful, please consider citing:
+
+```
+@article{VideoRFT,
+  title={VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning},
+  author={Wang, Qi and Yu, Yanrui and Yuan, Ye and Mao, Rui and Zhou, Tianfei},
+  journal={arXiv preprint arXiv:2505.12434},
+  year={2025}
+}
+```