用一个模型从原始多模态数据流中自动提取训练样本，省时省力还更准。
DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams
- 基于高阶意图生成对齐格式、有证据支持的训练数据。
- 270亿参数模型在数据贫瘠领域恢复59%原域性能，优于单领域专家。
- 统一模型部署成本更低，适合多场景通用任务开发人员使用。
原始多模态数据流丰富但嘈杂、冗余且与训练目标不匹配。当前将其转化为监督信号需依赖脆弱规则或反复调用专有视觉-语言模型，每次新增样本都产生额外成本。我们提出是否可一次性学习并复用该转换过程，并形式化为意图条件下的数据精炼：给定原始数据流和高层意图，模型需输出结构对齐、证据支撑的训练实例。在40亿、90亿和270亿参数规模上训练DataClaw0，发现五类异构领域是否共享同一模型取决于模型容量。小规模（40亿、90亿）下联合模型劣于各领域专家，而270亿规模下表现更优，交叉点约在180亿参数附近。在每领域数据量相同的匹配对比中，性能反转主要归因于跨域迁移而非数据量：模型在数据贫乏领域提升最大，甚至在完全未参与训练的领域恢复59%的原域性能。下游后训练任务（GUI导航、动作视频生成、时空VQA）再现此排序，且联合配置部署成本更低，仅需服务一个模型而非五个。代码已开源。
原文摘要 · Abstract (English)
Raw multimodal streams are abundant but noisy, redundant, and unaligned with any particular training objective. Turning them into supervision today means either brittle heuristics or repeatedly querying a proprietary vision-language model, a cost that recurs with every new sample. We ask whether this conversion can instead be learned once and reused, and formalise intent-conditioned Data Tailoring: given a raw stream and a high-level intent, a model must return schema-aligned, evidence-grounded training instances. Training DataClaw0 at 4B, 9B and 27B, we find that whether five heterogeneous domains should share one model depends on capacity. A jointly trained model is worse than per-domain experts at the two smaller scales and better at the largest, placing the crossover near 18B parameters. Matched-data comparisons, in which the joint model sees exactly the same data per domain as that domain's expert, attribute the reversal to cross-domain transfer rather than to data volume: it gains most where a domain is data-poor, and recovers 59\% of in-domain performance on domains withheld from training entirely. Downstream post-training reproduces this ordering on GUI navigation, action video generation and spatio-temporal VQA, and the joint configuration is also the cheaper to deploy, serving one model instead of five. Github: https://github.com/vancyland/DataClaw0
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