关于视觉抽象推理的数据合成与后训练
On Data Synthesis and Post-training for Visual Abstract Reasoning
AI总结：
本文提出创新的数据合成与后训练方法，使LLaVA-NeXT 7B模型在抽象视觉推理任务上显著超越GPT-4o等大模型，且保持通用多模态能力。
摘要：
本文是一项开创性工作，旨在解决大型视觉语言模型（VLMs）的抽象视觉推理（AVR）问题。我们使通用的LLaVA-NeXT 7B模型能够感知并推理特定的AVR问题，以显著优势超越了开源（如Qwen-2-VL-72B）和闭源的强大VLMs（如GPT-4o）。这是一项重大突破，因为几乎所有先前的VLMs在代表性AVR基准上均失败或表现近乎随机。我们成功的关键在于创新的数据合成与后训练过程，旨在充分缓解任务难度并逐步引导模型学习。我们的7B模型在AVR上表现良好，且未牺牲通用多模态理解能力。我们希望本文能作为该领域的早期探索，并激发抽象视觉推理的进一步研究。
英文摘要：
This paper is a pioneering work attempting to address abstract visual reasoning (AVR) problems for large vision-language models (VLMs). We make a common LLaVA-NeXT 7B model capable of perceiving and reasoning about specific AVR problems, surpassing both open-sourced (e.g., Qwen-2-VL-72B) and closed-sourced powerful VLMs (e.g., GPT-4o) with significant margin. This is a great breakthrough since almost all previous VLMs fail or show nearly random performance on representative AVR benchmarks. Our key success is our innovative data synthesis and post-training process, aiming to fully relieve the task difficulty and elicit the model to learn, step by step. Our 7B model is also shown to be behave well on AVR without sacrificing common multimodal comprehension abilities. We hope our paper could serve as an early effort in this area and would inspire further research in abstract visual reasoning.
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。
