| Thanks for your perspective from somebody working on the field. I still wonder though: what would the results be if we'd just use a richer dataset + more parameters? Would it be really that different results? (except costs, as MoE def helps with that) MoE: I assume some people just specialize in working with routing as with that, as by reducing the amount of params and just using a subset, you end up making it less costly. So, AI researchers are only working on optimizations on getting this better? Same question on Reasoning, so AI researchers are working mostly on optimizations on top of it, like CoT and so on, like mini-optimizations. So basically, they work on those micro-optimizations, put them together and see a % improvement in a benchmark? I'm sure this is probably awesome for languages, which if I'm not mistaken, it was the use-case initially used on "All you need is attention" and the entire LLM revolution. But this seems to be a very clear path to be "taking the car to the carwash by foot" for a long time, isn't it? It feels like we'll keep "taking the car to the carwash by foot" until somebody optimizes for that prompt, or some pre-training done, and then there'll be another prompt that will show that the AI has real trouble with very basic real-world reasoning and imagination. Isn't it the case, or do you see any kind of research that could take us from that plateau full of micro-optimizations that get us a few cm higher to the peak? |
I would not describe reasoning as optimization: In fact, it's typically doing the opposite, as models spend way more tokens (and therefore compute) on responding to the same prompt. Some of the smartest models these days use ridiculous amounts of reasoning before they ever respond. Try Deep Research in Gemini or Claude and you'll see what I'm talking about.
>> But this seems to be a very clear path to be "taking the car to the carwash by foot" for a long time, isn't it?
I thought the progress was plateauing sometime last year too, but then some new models got released and we saw that the multilingual capabilities improvements are real. And if you want something more tangible and reported on, consider the Opus 4.5/4.6 coding revolution (Claude Code explosion) a few months back.
LLMs being stochastic and statistical machines, there will always be funny things that people will come up with that will trick them, be it R's in strawberry or the carwash by foot. At the same time, I can tell you from my experience that a lot of the Misguided attention ( https://github.com/cpldcpu/MisguidedAttention ) type of stump questions work at a much lower rate with newer models. Progress is being made, it's just not in visible areas.
BTW, you can come up with many trick questions that will stump even humans with PhDs. They will be of different kind than the ones for LLMs, but this is not a flaw unique to LLMs.
If you're asking whether the progress to AGI isn't taking too long, then I personally think LLMs, at least with their current architecture, are not the foundation of AGI, and will always have inherent limitations. But we're fully in the "that's just like, your opinion, man" territory now :)