这篇论文点出了一个关键问题：AI生成文本的优化可能走偏了，不只是工程进步，还牵扯价值观判断。适合想理解技术背后隐患的人。
2019年OpenAI发布两百万GPT-2输出以检测机器生成文本，但这些输出不完整且含语法错误。论文认为当前对齐技术（损失函数、奖励模型、基准测试）只是优化文化的最新表现，无法区分低概率文本是错误还是创新。批评者指出这种优化范式在五年内取得了合法语言的裁判权，却缺乏判断力。
Optimization Is Not All You Need
In 2019, OpenAI released two million GPT-2 outputs-ungrammatical, half broken-to aid the detection of machine-generated text. The alignment that produced their more fluent successors is usually regarded as an engineering achievement; we read it instead as the newest expression of optimization culture: the conviction, older than the technology, that measurable improvement along predefined axes exhausts the question of value. Tracing that conviction through the stack-pretraining, decoding, preference tuning, benchmarking, interface-and back through its genealogy in the audit society, we arrive at the limit: an optimization procedure can measure how improbable a piece of generated text is; it cannot tell whether that unlikelihood is error or invention. A procedure that cannot make that distinction has nonetheless, within half a decade, assumed the authority to set the protocols of legitimate language. Held for centuries by academies and schoolrooms, grammars and examiners, this authority has been given over to loss functions, reward models, benchmarks, and system prompts: an apparatus that executes the office of judgment with no capacity for judging.