VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning
Qi Wang, Tianfei Zhou, Yanrui Yu, Ye Yuan, Rui Mao
Reinforcement fine-tuning (RFT) has shown great promise in achieving human-level 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 online 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 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 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. Code, model, and data will be released.