斯坦福教授用 Jev 做无人的 LLM 训练循环
系统在 Opus 5.5 上跑训练，Jev 看结果决定下一轮方向，好配置加算力、坏训练直接丢。
a Stanford professor built a Jev system that can train LLMs without any human researchers and sold it for $13,780 the system running on Opus 5.5 continuously trains different versions of a language model, evaluates their performance, and decides what should change in the next training cycle. Jev looks at the training results and decides which direction the next run should take. a configuration that improves the model gets more compute. a bad training run gets discarded. and promising changes trigger new experiments automatically. the system essentially turns LLM training into a self-improving feedback loop. I found a breakdown of the architecture they used, and the way Jev decides how the next training cycle should run is surprisingly clever. going to post how the system works next, so stay tuned if you want to see what Jev Engineering looks like inside an LLM training pipeline. would you let an AI decide how your LLM should be trained?
这条是引用着另一条写的：x.com/me_barnyx/status/2101976764779999381