重新思考视频中的链式推理
Rethinking Chain-of-Thought Reasoning for Videos
AI总结：
本研究提出了一种高效的视频推理框架，通过简洁推理和减少的视觉标记提升推理效率和性能，无需依赖传统CoT注释或监督微调。
摘要：
链式推理（CoT）在自然语言处理中解决复杂任务方面取得了巨大成功，而近期的多模态大语言模型（MLLMs）已将这一范式扩展到视频推理。然而，这些模型通常依赖于长推理链和大量输入视觉标记。受我们基准研究的实证观察启发，我们假设简洁的推理结合较少的视觉标记足以实现有效的视频推理。为了验证这一假设，我们设计并验证了一个高效的后训练和推理框架，以增强视频MLLM的推理能力。我们的框架使模型能够操作压缩的视觉标记，并在回答前生成简短的推理轨迹。结果模型在推理效率上有显著提升，在各种基准上表现竞争，且避免了对人工Co T注释或监督微调的依赖。总体而言，我们的结果表明，长而人类般的CoT推理可能不必要，简洁推理可以既有效又高效。我们的代码将在https://github.com/LaVi-Lab/Rethink_CoT_Video上发布。
英文摘要：
Chain-of-thought (CoT) reasoning has been highly successful in solving complex tasks in natural language processing, and recent multimodal large language models (MLLMs) have extended this paradigm to video reasoning. However, these models typically build on lengthy reasoning chains and large numbers of input visual tokens. Motivated by empirical observations from our benchmark study, we hypothesize that concise reasoning combined with a reduced set of visual tokens can be sufficient for effective video reasoning. To evaluate this hypothesis, we design and validate an efficient post-training and inference framework that enhances a video MLLM's reasoning capability. Our framework enables models to operate on compressed visual tokens and generate brief reasoning traces prior to answering. The resulting models achieve substantially improved inference efficiency, deliver competitive performance across diverse benchmarks, and avoid reliance on manual CoT annotations or supervised fine-tuning. Collectively, our results suggest that long, human-like CoT reasoning may not be necessary for general video reasoning, and that concise reasoning can be both effective and efficient. Our code will be released at https://github.com/LaVi-Lab/Rethink_CoT_Video.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
