DataClaw0：從原始數據流中進行代理式多模態數據定制

摘要

大規模非結構化多模態數據流存在高「數據熵」，既阻礙人類高效知識獲取，也影響高品質AI後訓練。現有的被動式標註範式高度依賴啟發式規則或通用視覺語言模型（VLM），成本高昂、形式單調，且無法釋放原始數據中蘊含的深層程序邏輯。我們將數據處理提升為可學習的能力，提出朝向**代理式數據裁剪**的範式轉移，即主動提煉並結構化數據，以對齊多樣的使用者與下游意圖。為克服訓練此類高階能力時的數據稀缺瓶頸，我們設計了一個兩階段流程，將生成式語義合成錨定於確定性**事實錨點**，進而生成涵蓋五大核心物理與數位領域的大規模數據集。在此基礎上，**DataClaw_0-9B**模型將監督式微調（SFT）與群體相對策略最佳化（GRPO）相結合，實現了對複雜提煉與裁剪意圖的穩健對齊。為系統量化此能力，我們建構了**DataClaw_0-val**，首個專注於數據提煉的評測基準。關鍵的是，我們採用下游後訓練作為最終驗證的試金石。在影片生成、真實世界視覺問答（VQA）與GUI導航上的評估結果證實，**DataClaw_0**能提供高資訊密度的裁剪數據，在有限訓練數據下促進模型高效適應新任務。專案頁面：https://czjdsg.github.io/MakeAnyData

English

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