这篇论文从GPT-2讲到对齐，批评优化文化把语言判断交给了损失函数，值得所有做AI的人反思。
2019年OpenAI发布了200万个GPT-2的不完整输出以辅助检测机器生成文本。作者将后续的对齐技术视为优化文化的新表现，认为它继承了那种相信可测量改进能穷尽价值问题的信念。通过追溯预训练、解码、偏好调优、基准测试和接口中的优化逻辑，论文指出优化过程能衡量生成文本的不可概率性，却无法区分这种反常是错误还是创新。然而在过去五年内，这一缺乏判断能力的优化机制已取代学术和语法机构，掌握了设定合法语言协议的权威。
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.