OpenAI RLHF 联合创作者创立 Jev，押注可靠性而非更大模型
搞 RLHF 的元老出来讲了大实话：现在的模型是给人类打下手优化的，自动化会翻车。他的新公司 Jev 押可靠性而不是堆参数，如果你在做无人值守的企业流程，这套选任务的方法论很值得听。
曾参与 RLHF 和 ChatGPT 研发的 OpenAI 前成员 @CompleteSkeptic 在 #AIECode 大会上分享了他对 LLM 的判断：当前模型为人类在环的辅助场景优化，而非自动化。他用"95% Einstein, 5% Mr. Bean"形容这种不一致——对聊天机器人可以接受，对后台决策任务则是致命缺陷。
他的核心观点引申自 Rich Sutton 的 Bitter Lesson：算力胜过算法、数据胜过算力，而最"苦"的部分是选对任务。他据此创立了公司 Jev，路线不是把模型做得更大或更便宜，而是让系统达到可依赖的可靠性。
这条思路把 LLM 从"助手"重新定位为"自动化执行者"，与主流堆参数、压成本的路径形成区分，对需要在无人干预下运行的企业决策场景有直接参考价值。
The most first-principles talk at #AIECode came from @CompleteSkeptic — who co-created RLHF/ChatGPT at OpenAI and now builds Jev. His point: today’s LLMs are optimized for assistance (a human in the loop), not automation. “95% Einstein, 5% Mr. Bean” is fine for a chatbot and fatal for a back-office decision. The Bitterest Lesson: compute beats algorithms, data beats compute, and the bitterest part is picking the right task. Jev’s bet: not bigger, not cheaper, but reliable.📊 2 ⚡
This report is categorized under AITOP's "Products" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Jev, OpenAI, RLHF, LLM可靠性.
Source attribution: curated from AI Will (@FinanceYF5). Cross-source corroboration: 11 independent sources have covered this development (including 爱范儿, TestingCatalog, OpenAI Blog), confirming industry-wide signal strength.
