Or to write it less pessimistically, the models are trained to prime their own context window such that by the end of the chain they arrive at more valuable responses. By creating intermediary steps in the chain, the next step is easier to generate rather than moving directly to the desired response. We call it reasoning because it is intuitively analogous to human reasoning methods though it is understood that LLMs don't succeed as generally as humans are able to.

Progress is hard to keep track of in this fast paced environment, but aren't there already models that can add external tools and simply offload parts of he reasoning there? Maybe over MCP or some other mechanism, so it can offload e.g. calculations, or test code in a sandbox, or even write code to answer part of a question, execute the code somewhere, and take the results into the rest of the inference process as context?

Or is there a more subtle issue which prevents or makes this hard?

Is there something fundamentally impossible about having a model detecting the amount of Rs in 'strawberry' to be a string search operation and in some sandbox execute something like:

% echo "strawberry" | tr -dc "r" | wc -c

       3

It seems agents do this already, but regular GPT style environments seem to lack it?

Let's break this down carefully, step by step.

Start with Jacob.

Jacob’s son → call him A.

A’s son → call him B.

B’s son → call him C.

C’s son → call him D (this is “the son of Jacob’s son’s son’s son”).

Now the question asks for the paternal great-great-grandfather of D:

D’s father → C

D’s grandfather → B

D’s great-grandfather → A

D’s great-great-grandfather → Jacob

Answer: Jacob

I won't read this because you're not really thinking, just pressing keyboard keys.

Thank you, I do not have a "reasoning" LLM and I have not found LLMs very useful in my life so I do not really engage with them outside of reading about them here and in other places.

This isn't an explanation. Just another "AI bad!" comment.