VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning

• URL: http://arxiv.org/abs/2505.12434v2
• Date: Wed, 25 Jun 2025 07:35:51 GMT
• Title: VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning
• Authors: Qi Wang, Yanrui Yu, Ye Yuan, Rui Mao, Tianfei Zhou,
• Abstract summary: VIDEORFT is a novel approach to cultivate human-like video reasoning capabilities in MLLMs.<n>It 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.<n>It achieves state-of-the-art performance on six video reasoning benchmarks.
• Score: 33.170426237654596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 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 VIDEORFT achieves state-of-the-art performance on six video reasoning benchmarks.

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