The recent 'Stolen Thoughts'[1] paper shows many excerpts of private reasoning for frontier models.
For a complex maths problem, Sol reasoned in 367 tokens before working:
We need solve. Need interpret no person sits next to two other people = among binary string length16 weight8, no occupied chair whose both neighbors occupied, equivalently ab 111 substring. Endpoints cannot have two neighbors anyway; only avoid 111. Count binary strings length16 weight8 avoiding 111. Need N mod1000. Compute stepwise perhaps runs of 1 length max2. Count via runs.
[... cut in half for HN readability ... ]
Check interpretation potentially "no person sits next to two other people": no seated person's chair adjacent to two occupied chairs. Exactly no three consecutive chairs selected. yes.
Need reason step by step final boxed 907. Explain runs. Ensure people each select chair distinct subset (given subset count). Let's present.
I think one of the bigger problems is that verbose, `max`-style thinking does generally lead to higher benchmark scores. And model vendors are incentivised to for benchmarks (at least to some extent).
Openai has been focusing a lot on cutting down overthinking is the feel I get. If you look at the artificial analysis tokens per task benchmark Sol especially at lower effort uses far less tokens than the competition.
All these weird partial language thought patterns im surprised none of the teams have taught the models to think in something like court stenography or some very dense pattern (i mean they even tried caveman language at one point)
It's not obvious if that would help. Some tests have shown that what exactly the thinking tokens are only makes a small difference to the performance of the model, and that the contents of them are sometimes only tenuously related to to what the model actually does after them. It seems like it could be they are more like a kind of "mumbling" and that the underlying mechanism by which they actually help performance is just that it makes more computation available to the model by just giving more passes on the earlier input tokens through the network.
Keep in mind that the model is thinking in a token space, itself a compressive representation of language.
(Note: there's still a huge grammar penalty, so, ugh do think small.)
The real breakthrough is going to be thinking in latent space.
Arguably this is already happening: the whole state of the model gets fed through from token to token, and even just shoving a bunch of dashes in between the input tokens and the model's output can improve performance (thinking tokens from the model help a little bit more, but the difference is not as large as you mught expect).
It selects tokens but they expand to embedding vectors which are huge, also in memory and attention requirements, I think?
One of the main goals at the moment is to keep thinking human legible. You can imagine how much harder it’d be to do root cause analysis on the recent OpenAI event if we couldn’t even tell what they’re thinking.
I wonder if a human learning to mimic this thinking style work would improve their thinking ability?