VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning

• URL: http://arxiv.org/abs/2504.06958v3
• Date: Sun, 13 Apr 2025 09:52:21 GMT
• Title: VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning
• Authors: Xinhao Li, Ziang Yan, Desen Meng, Lu Dong, Xiangyu Zeng, Yinan He, Yali Wang, Yu Qiao, Yi Wang, Limin Wang,
• Abstract summary: This paper aims to enhance video perception with Reinforcement Fine-temporalning (RFT)<n>We develop VideoChat-R1, a powerful video MLLM that achieves state-the-art performance on-temporal tasks without sacrificing chat ability.<n>Our findings underscore the potential of RFT for specialized task enhancement of Video MLLMs.
• Score: 42.316341452766075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in reinforcement learning have significantly advanced the reasoning capabilities of multimodal large language models (MLLMs). While approaches such as Group Relative Policy Optimization (GRPO) and rule-based reward mechanisms demonstrate promise in text and image domains, their application to video understanding remains limited. This paper presents a systematic exploration of Reinforcement Fine-Tuning (RFT) with GRPO for video MLLMs, aiming to enhance spatio-temporal perception while maintaining general capabilities. Our experiments reveal that RFT is highly data-efficient for task-specific improvements. Through multi-task RFT on spatio-temporal perception objectives with limited samples, we develop VideoChat-R1, a powerful video MLLM that achieves state-of-the-art performance on spatio-temporal perception tasks without sacrificing chat ability, while exhibiting emerging spatio-temporal reasoning abilities. Compared to Qwen2.5-VL-7B, VideoChat-R1 boosts performance several-fold in tasks like temporal grounding (+31.8) and object tracking (+31.2). Additionally, it significantly improves on general QA benchmarks such as VideoMME (+0.9), MVBench (+1.0), and Perception Test (+0.9). Our findings underscore the potential of RFT for specialized task enhancement of Video MLLMs. We hope our work offers valuable insights for future RL research in video MLLMs.

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