知識蒸餾對中國大語言模型（LLM）到底有多重要？回應 Anthropic 的「蒸餾攻擊」觀點
Original: How much does distillation really matter for Chinese LLMs?
Anthropic recently published research on "distillation attacks," defining the practice of external developers using its API outputs to…
本文探討知識蒸餾（Distillation）在中國大語言模型（如 DeepSeek、Qwen）發展中扮演的角色。針對 Anthropic 近期將蒸餾視為「安全攻擊」的報告，作者指出，雖然蒸餾確實加速了模型對齊，但中國 LLM 的成功更多歸功於其強大的預訓練底座與強化學習（RL）創新。將蒸餾單純簡化為「抄襲」或「攻擊」，忽略了其作為標準機器學習技術的本質，也低估了中國團隊的工程實力。
Anthropic recently published research on "distillation attacks," defining the practice of external developers using its API outputs to train other models as a potential security threat and intellectual property violation. This has reignited fierce debate in Western tech circles about whether Chinese large language models (LLMs) like DeepSeek and Qwen are "built entirely by distilling American models."
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