[solid_fuel]

My two cents - I believe that achieving anything close to AGI will require a significant change in architecture. A bifurcated system with a fully internal reasoning loop makes sense, but I don't think you could train one.

Something like

    f(u, t) -> (u', t')

where u is english text and t is an internal "thinking" loop.

Currently we train models by feeding them sample text and then tweaking the weights until the predicted next token matches the expected next token from the input text. This works well because LLM corps were able to steal vast quantities of sample text from the internet.

But, if you also have an internal reasoning loop, how do you train that part? The internal loop is not necessarily going to produce one clean token for a given input like an LLM does, and the time scale isn't going to be the same (meaning an internal loop might be expected to run 10 times for every one token produced). There is no "correct next token" for the internal reasoning loop. This is roughly the same training issue that killed RNNs.