[CAP_NET_ADMIN]

LLMs can be trained on all the math books in the world, starting from the easiest to the most advanced, they can regurgitate them almost perfectly, yet they won't apply the concepts in those books to their actions. I'd count the ability to learn new concepts and methods, then being able to use them as "reasoning".

[margorczynski]

Aren't there quite a few examples of LLMs giving out-of-distribution answers to stated problems? I think there are two issues with LLMs and reasoning:

1. They are single-pass and static - you "fake" short-term memory by re-feeding the questions with it answer 2. They have no real goal to achieve - one that it would split into sub-goals, plan to achieve them, estimate the returns of each, etc.

As for 2. I think this is the main point of e.g. LeCun in that LLMs in themselvs are simply single-modality world models and they lack other components to make them true agents capable of reasoning.

[intended]

its those kinds of examples that make it hard to cleave a measurement of success.

Based on those kinds of results an LLM should, in theory, be able to plan, analyze and suggest improvements, without the need for human intervention.

You will see rudimentary success for this as well - however, when you push the tool further, it will stop being... "logical".

I'd refine the point to saying that you will get some low hanging fruit in terms of syntactic prediction and semantic analysis.

But when you lean ON semantic ability, the model is no longer leaning on its syntactic data set, and it fails to generalize.