Smaller models seem to be more complex. The encoding, reasoning, and decoding functions are more entangled, spread across the entire stack. I never found a single area of duplication that generalised across tasks, although clearly it was possible to boost one ‘talent’ at the expense of another. But as models get larger, the functional anatomy becomes more separated. The bigger models have more ‘space’ to develop generalised ‘thinking’ circuits, which may be why my method worked so dramatically on a 72B model. There’s a critical mass of parameters below which the ‘reasoning cortex’ hasn’t fully differentiated from the rest of the brain.
Российский врач вернется к работе после истекшей кровью пациентки14:48
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GPs already set aside a chunk of their daily appointments to try to ensure patients who need an immediate appointment can get one.
甲骨文与OpenAI或放弃扩建得州大型AI数据中心