[YeGoblynQueenne]

>> It took a real algorithmic advancement - reasoning models - to solve it.

You could only claim that if the numbers of parameters and training tokens remained constant while "reasoning" was added to the base models, which is not the case.

So if you look at the ARC-AGI-1 leaderboard (https://arcprize.org/leaderboard), you can clearly see that the bigger a model the better it performs, and that's for the "reasoning" models, e.g. looking at the graph, Claude Opus 4 is at ~30%, Opus 4.5 is between ~60% and ~80% and Claude 4.7 is at ~90% [1].

Not surprising: LLMs continue to improve in performance as long as more resources are spent to train them. "Algorithmic" advances would show the trend line going the other way, i.e. tokens and parameters decreasing steadily while performance either staying the same or improving.

If you've observed something like that I'll be happy to be corrected but I haven't.

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[1] Incidentally, Opus 4.6 slightly outperforms 4.7 and 4.8 with their -alleged- reduced total parameter count. 4.6 is at 94.0% while 4.7 is at 93.5% and 4.8 at 92.5%.

[Legend2440]

I don't agree with your definition.

The point of reasoning models is that some tasks fundamentally require a certain number of serial steps. Base models are limited to learning parallel algorithms because of their parallel training, and so struggle on inherently-serial tasks like solving logic puzzles.

The advancement from reasoning is that it allows LLMs to learn a broader class of algorithms.

[YeGoblynQueenne]

Sorry, which definition do you mean?

The problem with "reasoning" is that for the most part it happens outside of training e.g. as CoT. The base model knows what it knows, it knows what it's trained on, and it can't really go much farther than that.

Now, starting with o3, "reasoning" models are probably (who knows exactly) trained on traces of reasoning, either from automated systems or from human experts, but that doesn't mean they learn any kind of algorithm, just that they learn to reproduce the behaviour of "algorithms" (or of reasoning humans). That can improve performance on certain kinds of task (the ones in the training set) up to a point, but you're not going to make a tiny model perform like one ten times larger just by that.

On the other hand, I do think that LLMs can be made smaller without losing a commensurable amount of performance, like e.g. the Tiny Recursion Model (TRM) which did OK at ARC 1 (45% I think). But then you lose a lot of functionality also. Basically the larger models probably have more parameters than they really need and that's something the industry seems to have realised, but you still need huge parameter counts to reach top performance anyway.

And then there's the training tokens, which aren't getting any fewer.

[Legend2440]

>but you're not going to make a tiny model perform like one ten times larger just by that.

Small reasoning models do indeed outperform base models that are 10x larger, at these logic/reasoning tasks that require serial computation.

They do not outperform at tasks that rely more on world knowledge or memorization.

In most cases the base model cannot complete logic tasks at all, or only for very small instances; it's reasoning or nothing.

> but that doesn't mean they learn any kind of algorithm

They do indeed learn algorithms and can step through them with CoT. This is what allows reasoning models to, e.g. reliably multiply large numbers by applying the grade-school multiplication algorithm.