[wild_egg]

There is an amount of brute forcing that becomes possible at those speeds that I think could even take us beyond 80%. If we could have Qwen3.6-27B running at 15k t/s, run 100 attempts concurrently, select top-K solutions and synthesize a final result from them.

There was a paper a while back that showed top-K selection like that with tiny models was able to reliably solve some 1M-step Tower of Hanoi when no frontier model could. Very big level up in capability just from horizontally scaling compute.

[yomismoaqui]

100 dumb folks don't make an Einstein

[wild_egg]

You pull out Einstein when you need a breakthrough.

[Schlagbohrer]

To extend this metaphor or the "million monkeys typing at a million keyboards", it occurs to me that if you can just slightly increase the intelligence of each sub-agent by a tiny amount, the overall end product can shoot up in quality tremendously.

If I can make my small fast AI model just a tiny, tiny bit more capable, and still run 100 of them or 1000 and run an evaluation model on top of that, the overall system capability will scale quickly with tiny increases in base model intelligence.

[mdp2021]

But some (Meta, Anthropic) suggested that optimizing and extending the "<think>" process can produce extra value. (I do not know if that requires an improved underlying architecture - frontier models architectures are sometimes not public.)