大模型防蒸馏政策效果存疑：蒸馏只是独立实验室的数据捷径，封锁无法阻止中国AI追赶

大模型防蒸馏政策效果存疑：蒸馏只是独立实验室的数据捷径，封锁无法阻止中国AI追赶

2026-06-17 18:39View Original

ChainThink 消息，针对华盛顿与 Anthropic 试图通过封锁前沿模型切断中国大模型「蒸馏」通道的举措，前 GitHub 国际化战略负责人、Interconnected Capital 创始人 Kevin S. Xu 指出，对抗性蒸馏只是部分中国独立实验室在数据饥饿下的无奈捷径，靠封锁 API 根本无法阻挡中国 AI 的整体进展。

被点名的 DeepSeek、月之暗面与 MiniMax 均为缺乏集团生态支持的独立实验室，面临推理步骤等高质量后训练数据匮乏的硬伤。

背靠阿里（Qwen）、字节（Seed）或小米的大厂实验室，拥有不输谷歌与苹果的自有海量场景数据，并不依赖蒸馏，封锁政策顶多给独立实验室带来短期阻碍，无法动摇中国大厂的根基。

外界盛传的中国「数据优势」实为误解，在训练前沿大模型所需的高质量知识标注和评测数据上，中国不仅没有优势，反而严重缺乏类似 Scale AI 或 Surge 这样成熟的商业化数据供应链。

因国内数据服务商质量低下，独立实验室才将 API 蒸馏作为廉价的数据获取策略。数据标注产业属于低门槛的商业模式问题，并非技术硬伤，国内供需缺口很容易被填补。

从长远看，纯蒸馏的学生模型理论上限无法超越老师，但中国开发者最终会打破这一上限，设计出超越导师的大模型，美方的封锁政策不仅无效，反而可能过早切断能将中国模型锁死在「学生」天花板下的理论钳制。

Disclaimer: Contains third-party opinions, does not constitute financial advice

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