Like does reasoning find a gradient to optimize a solution? Or are they just trying to expand state until finding what the LLMs world knowledge would say is highest probability?
For example, I can imagine an LLM reasoner might run out of state trying to perfectly solve for 50 intricate unit tests. Because it ping pongs between solving one case, then another, playing whack-a-mole and not converging.
Maybe there's an "oh duh" answer to this, but where I struggle with the limits of agentic work vs traditional ML.
Does it ever respond when you ask it something? I started a query at https://www.k2think.ai/guest thirteen minutes ago and haven't gotten an answer yet.
Debunking the Claims of K2-Think https://www.sri.inf.ethz.ch/blog/k2think
Thanks for sharing that post here!
Can reasoners be optimizers?
Like does reasoning find a gradient to optimize a solution? Or are they just trying to expand state until finding what the LLMs world knowledge would say is highest probability?
For example, I can imagine an LLM reasoner might run out of state trying to perfectly solve for 50 intricate unit tests. Because it ping pongs between solving one case, then another, playing whack-a-mole and not converging.
Maybe there's an "oh duh" answer to this, but where I struggle with the limits of agentic work vs traditional ML.
They can be, in the same way humans can be optimizers.
In most cases, there's no explicit descent - and if any descent-like process happens at all, it's not exactly exposed or expressed as hard logic.
If you want it to happen consistently, you add scaffolding and get something like AlphaEvolve at home.
Does it ever respond when you ask it something? I started a query at https://www.k2think.ai/guest thirteen minutes ago and haven't gotten an answer yet.
[dead]
https://venturebeat.com/ai/k2-think-arrives-from-uae-as-worl...
So currently what are the best OSS reasoning models? (and how much compute the needed)