AI学会自己整理数据了，不再靠人工规则
现在的AI处理视频、图片、文字混在一起的数据时，要么靠人工写规则，要么用通用模型硬套，又贵又死板。这篇论文让AI学会主动整理数据——像人一样从原始流里挑出关键、理清逻辑。他们用两步法生成训练数据：先定好事实锚点，再让AI自己合成多样化的整理任务。最终模型DataClaw_0在视频生成、问答、GUI导航等下游任务中，用更少的数据就达到了更好效果。它不是你明天能用上的，但方向很明确：未来AI会自己把混乱的数据变成高质量训练材料，减少人工标注的依赖。
📄 原文摘要(英文)
Massive unstructured multimodal streams suffer from high "data entropy," impeding both efficient human knowledge acquisition and high-quality AI post-training. Existing passive annotation paradigms, heavily reliant on heuristic rules or general VLMs, are costly, monotonous, and fail to unlock the deep procedural logic embedded in raw data. We elevate data processing to a learnable capability, proposing a paradigm shift towards Agentic Data Tailoring, which actively refining and structuring data to align with diverse user and downstream intents. To overcome the data scarcity bottleneck in training such high-order capabilities, we design a two-stage pipeline grounding generative semantic synthesis in deterministic Factual Anchors, yielding a large-scale dataset spanning five core physical and digital domains. Building upon this, DataClaw_0-9B model synergizes Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO), achieving robust alignment with complex refinement and tailoring intents. To systematically quantify this capability, we construct DataClaw_0-val, the first benchmark dedicated to data refinement. Crucially, we adopt downstream post-training as the ultimate validation touchstone. Evaluations on video generation, real-world VQA, and GUI navigation confirm that DataClaw_0 delivers high-information-density tailored data, facilitating efficient model adaptation to new tasks under limited training data regimes. Project page: https://czjdsg.github.io/MakeAnyData