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.

That doesn't look like an overthinker to me, and matches my experiences. There's plenty of papers and research on reducing thinking verbosity/length while keeping as much quality as possible.

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).

[1] https://stolen-thoughts.com/

▲ markasoftware 4 hours ago | parent | next [-]

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.

▲ cchance 5 hours ago | parent | prev | next [-]

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)

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robkop 7 minutes ago | parent | next [-]

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.

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chaboud 4 hours ago | parent | prev [-]

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.)

	▲	qeternity 2 hours ago \| parent \| next [-]
		The real breakthrough is going to be thinking in latent space.
	▲	kzrdude 2 hours ago \| parent \| prev [-]
		It selects tokens but they expand to embedding vectors which are huge, also in memory and attention requirements, I think?

▲ brador an hour ago | parent | prev [-]

I wonder if a human learning to mimic this thinking style work would improve their thinking ability?