I don’t think there’s a fundamental reason that performance has to be monotonic in model size or even training FLOPs. At least I don’t think it’s been proved to be so, so I think “misinformed” is a bit premature and sort of makes GP’s point.

There’s evidence that model size and representational capacity are not exactly the same, and that scale is maybe more important for learning than it is for representation (past a point). Consider the early work from the current neural scaling paradigm. The Chinchilla scaling study shows that smaller models can match the performance of larger models by training longer.

To GP’s point, if everyone is exploiting the scaling lever, few resources are being allocated to finding more efficient training algorithms that could let us work with right-sized models instead of pulling the scaling lever as hard as we can afford to.

I’ll end with a dramatic example from my field of materials science (which admittedly might not strictly generalize to LLMs). A lot of the field is pursuing the model scaling strategy, and it’s still paying off. But [0] recently reported competitive accuracy with much smaller models that run faster and can address much larger problems. The model architecture is pretty much the same, but they use a different training strategy and really focus on data quality

0: https://arxiv.org/abs/2504.21286

I'm not saying that it is impossible for a more advanced model and training paradigm to outperform a larger model, what I'm saying is that if you leave everything the same except model size, the larger model nearly always outperforms the smaller one. This is intuitively true if you consider that you can fit in the smaller model + extra parameters into a larger model. There are some cases in which it doesn't work due to dataset/model size incompatibilities leading to overfitting, this is mostly covered by the neural scaling laws.

Based on a quick cursory glance at your example: Better data + better technique led to a better result with less parameters. Would you assume that then scaling both the dataset and model once again would lead to even better results? If you haven't fully encompassed the underlying distribution with datapoints, intuition says yes.

What I wanted to initially highlight was actually something slightly different: Specifically that people keep trying to "outsmart" optimizers by either fully hand-crafting solutions or skewing existing machine learning algorithms via additional tricks that are supposed to encode "human intuition" or something similar (to be fair there are ways to do it correctly). These all tend to fall short in a few years simply due to "line go up" being stupidly effective (compute getting cheaper, more training data being available, better optimization strategies, better architectures) [0]

Specifically this idea of small fine tuned LoRA models falls into the trap quite often: People assume you can beat the big, slow, general purpose LLMs with a small highly specialized model that has been fine tuned on the "good" human intuition of your special inhouse dataset.

LoRA can do great things, but it is often misunderstood what LoRA actually does.

0: http://www.incompleteideas.net/IncIdeas/BitterLesson.html

I especially wonder about the quality of N big models in a delegation harness against M smaller models in a delegation harness with N and M tuned to use the same amount of total compute. (So a few agents of big models against a lot of big models). I wouldn't be surprised if the advantages of delegation and the corresponding compression of context outweighs the lower quality of the smaller models.

Or put differently a wide exploration of long chains of obvious insights might be more valuable than a more narrow exploration of shorter chains of deeper insights. But perhaps that is a wrong sense of the difference between a small model and a large model.