this post was submitted on 10 Oct 2026
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Backpropagation, the algorithm behind current machine learning systems, precludes an active world-model; it can't learn during inference.
I don't believe there's any requirement to learn during a particular part of the lifecycle? And if there was, the short term memory of a context window fulfils that
I thought learning was an important part of intelligence.
Huh, didn't know there was a word for it, thanks.
You need to be more specific. If your point is that you literally cannot backprop while doing a forward pass, then sure. But you can certainly do inference then backprop on the outcome...
I fail to see how that is substantially different from a human doing something then reflecting and learning.
You can critique LLMs and transformers generally, but to say it's somehow a problem with backprop is a bit of a stretch.
My read is that the user is saying that creating a world view contains trial and error. Online learning if you will. With the current setup this learning is very much not like this. While that makes a human to machine analogy I am also not too convinced by this reasoning. Just allow for a larger time lag and then the inference and learning is at the same scale.