2 ms·

But in theory you can make an LLM A LOT faster than a human. You can also run massive amount of LLMs in parallel. There might be a limit to a normal LLM but n

But in theory you can make an LLM A LOT faster than a human. You can also run massive amount of LLMs in parallel. There might be a limit to a normal LLM but not to theo everall system.

tavavex 2mo ago
Humans, however, are highly variable, which may produce really varied and interesting results if they work together. One instance of an LLM is the same as another instance, so while you may get more out of it by stacking more of them, I strongly suspect it falls victim to diminishing returns. 100 instances of the same LLM may converge on the same result as 10.
- boorang 2mo ago
  I think using different AGENTS.md can give the same model different perspectives on the same problem. For example a model with a well-tuned AGENTS.md by an expert mathematician approaching the same problem as the same model with a well-tuned AGENTS.md by an expert biologist can grind on the same problem from different perpectives. It's worth a shot at least, as a microservices architect I have a bias that we aren't networking these enough, a single main agent session orchestrating multiple subagents is different from multiple main agent sessions with their own subagents coordinating with each other.
  - tavavex 2mo ago
    Crucially, does it make capabilities infinitely scalable? My comment just said that models may have a hard cap, and maybe doing specific setups like yours can make reaching it easier, but making the 'team' 10x larger after that optimal point may bring few to no improvements. Although I'm also not sure about just how much better models can really get with this technique. Ultimately you're still getting the same model with the same training data, which are the important parts. Asking it to pretend to be something feels like it would just put a color filter in front of the conclusion the model has already predicted, or maybe alter the path to the conclusion slightly or pick a less likely answer that it still could've provided normally.
    - boorang 2mo ago
      I agree it doesn't make the capabilities infinitely scalable, wasn't arguing with that point. It's just an experiment. I'm not talking about "you are an expert mathematician, go", I'm talking about an expert encoding their heuristics into the AGENTS.md base context. Routing the model's attention to very different aspects of the same problem in the early context. FWIW I mean if I have an AGENTS.md that encodes my software heuristics (use an interface in situations like X, here's how we name variables, etc.) it generates far cleaner code than if I don't. Edit- mostly pointing out that stacking 10 base models vs. 10 models with sufficiently different base context isn't necessarily the same attention routing. I suppose I was thinking about tasks that don't have a concrete single answer.
- NitpickLawyer 2mo ago
  > 100 instances of the same LLM may converge on the same result as 10. Not in the highly verifiable domains. There you can take it from say 80-90% maj@x to 99% pass@n. Math, some parts of programming and cybersec are examples of highly verifiable domains. (e.g. if you're searching for a linux LPE, that's expensive to search but easy/cheap to verify - just have a token in /root and have the model retrieve that token)
  - tavavex 2mo ago
    Verifiability makes it easier to understand how well the LLM works, but this doesn't counter my hypothesis. If X number of instances get 99.0% on an objective, verifiable metric, is there any guarantee that 10X will get 99.9%? The fact that we are reliant on new model releases to push capability in big ways, and that people running gigantic clusters of LLMs end up beaten by new models implies that the capabilities of a given model have a hard upper limit, and that it may not even take much to reach it.
- Manfrednotfunny 2mo ago
  You can change the temperature if you like. Have a 1000 agents being 'normal' and 10 being chaotic.
  - tavavex 2mo ago
    High temperature makes the LLM pick more out-of-distribution tokens, but the choices its presented with are still the same or same-ish. I'm not convinced that the more random outputs don't end up averaging to roughly the same conclusion after enough passes.
margalabargala 2mo ago
> There might be a limit to a normal LLM but not to theo everall system. Bigger limit and no limit are very different.
ozgung 2mo ago
Yes, given enough time I can answer all the questions in an IQ test correctly. We measure human intelligence in a time-limited setting and score relative to the performance of other humans doing the exact same task. Problem is brains can’t be scaled. To scale humans we need organizations, but human organizations also don’t scale well with increasing headcount. LLMs scale well in almost all dimensions. Context window (working memory) can be a bottleneck but for humans you can’t scale it at all.

But in theory you can make an LLM A LOT faster than a human. You can also run massive amount of LLMs in parallel. There might be a limit to a normal LLM but not to theo everall system.

tavavex 2mo ago

Humans, however, are highly variable, which may produce really varied and interesting results if they work together. One instance of an LLM is the same as another instance, so while you may get more out of it by stacking more of them, I strongly suspect it falls victim to diminishing returns. 100 instances of the same LLM may converge on the same result as 10.

boorang 2mo ago
I think using different AGENTS.md can give the same model different perspectives on the same problem. For example a model with a well-tuned AGENTS.md by an expert mathematician approaching the same problem as the same model with a well-tuned AGENTS.md by an expert biologist can grind on the same problem from different perpectives. It's worth a shot at least, as a microservices architect I have a bias that we aren't networking these enough, a single main agent session orchestrating multiple subagents is different from multiple main agent sessions with their own subagents coordinating with each other.
- tavavex 2mo ago
  Crucially, does it make capabilities infinitely scalable? My comment just said that models may have a hard cap, and maybe doing specific setups like yours can make reaching it easier, but making the 'team' 10x larger after that optimal point may bring few to no improvements. Although I'm also not sure about just how much better models can really get with this technique. Ultimately you're still getting the same model with the same training data, which are the important parts. Asking it to pretend to be something feels like it would just put a color filter in front of the conclusion the model has already predicted, or maybe alter the path to the conclusion slightly or pick a less likely answer that it still could've provided normally.
  - boorang 2mo ago
    I agree it doesn't make the capabilities infinitely scalable, wasn't arguing with that point. It's just an experiment. I'm not talking about "you are an expert mathematician, go", I'm talking about an expert encoding their heuristics into the AGENTS.md base context. Routing the model's attention to very different aspects of the same problem in the early context. FWIW I mean if I have an AGENTS.md that encodes my software heuristics (use an interface in situations like X, here's how we name variables, etc.) it generates far cleaner code than if I don't. Edit- mostly pointing out that stacking 10 base models vs. 10 models with sufficiently different base context isn't necessarily the same attention routing. I suppose I was thinking about tasks that don't have a concrete single answer.
NitpickLawyer 2mo ago
> 100 instances of the same LLM may converge on the same result as 10. Not in the highly verifiable domains. There you can take it from say 80-90% maj@x to 99% pass@n. Math, some parts of programming and cybersec are examples of highly verifiable domains. (e.g. if you're searching for a linux LPE, that's expensive to search but easy/cheap to verify - just have a token in /root and have the model retrieve that token)
- tavavex 2mo ago
  Verifiability makes it easier to understand how well the LLM works, but this doesn't counter my hypothesis. If X number of instances get 99.0% on an objective, verifiable metric, is there any guarantee that 10X will get 99.9%? The fact that we are reliant on new model releases to push capability in big ways, and that people running gigantic clusters of LLMs end up beaten by new models implies that the capabilities of a given model have a hard upper limit, and that it may not even take much to reach it.
Manfrednotfunny 2mo ago
You can change the temperature if you like. Have a 1000 agents being 'normal' and 10 being chaotic.
- tavavex 2mo ago
  High temperature makes the LLM pick more out-of-distribution tokens, but the choices its presented with are still the same or same-ish. I'm not convinced that the more random outputs don't end up averaging to roughly the same conclusion after enough passes.

margalabargala 2mo ago

> There might be a limit to a normal LLM but not to theo everall system. Bigger limit and no limit are very different.

ozgung 2mo ago

Yes, given enough time I can answer all the questions in an IQ test correctly. We measure human intelligence in a time-limited setting and score relative to the performance of other humans doing the exact same task. Problem is brains can’t be scaled. To scale humans we need organizations, but human organizations also don’t scale well with increasing headcount. LLMs scale well in almost all dimensions. Context window (working memory) can be a bottleneck but for humans you can’t scale it at all.