This repository contains the VideoRFT model, presented in the paper VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning.

  📖 ArXiv    │   📀 CoT Dataset    │   📀 RL Dataset    │   🤗 Models

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 VideoRFT, a novel approach that extends the RFT paradigm to cultivate human-like video reasoning capabilities in MLLMs. 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 fully automatic CoT curation pipeline. First, we devise a cognitioninspired 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 visual-language model 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 strength the RL phase, we introduce a novel semantic-consistency reward that explicitly promotes the alignment between textual reasoning with visual evidence. This reward encourages the model to produce coherent, context-aware reasoning outputs grounded in visual input. Extensive experiments show that VideoRFT achieves state-of-the-art performance on six video reasoning benchmarks.

To overcome the scarcity of video CoTs, we develop a scalable, cognitively inspired pipeline for high-quality video CoT dataset construction.

To further strength the RL phase, we introduce a novel semantic-consistency reward that explicitly promotes the alignment between textual reasoning with visual evidence.

Based on above pipeline, we construct two large-scale datasets, i.e., 📀VideoRFT-CoT-102K and 📀VideoRFT-RL-310K.

We begin with supervised fine-tuning on the VideoRFT-CoT dataset for one epoch:

This step can be skipped by directly using our pretrained SFT models, available at 🤗VideoRFT-SFT-7B or 🤗VideoRFT-SFT-3B.

Next, perform reinforcement learning using the VideoRFT-RL dataset:

To enable faster training via vLLM acceleration:

All frame-related configurations can be adjusted in src/qwen-vl-utils.

You can configure these parameters in src/qwen-vl-utils.

We gratefully acknowledge the contributions of the open-source community, particularly DeepSeek-R1, Open-R1, and R1-V.

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}
}