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type. In cases like U16 value cast as I16 -> I64, narrowing could
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Fast branchless asin(x) approximation.
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.
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A separate post had the same details for the latter drone, but it was unclear if that was a different event.