> Humans, by contrast, have ~100T parameters (counting each synapse as a weight) and learn from just ~200M tokens, a tokens per parameter of 1:500,000.
I don’t think that’s a fair comparison since (as far as I know) humans aren’t born with randomly initialised neural weights that are setup in a very generic structure, but have at the very least extremely strong inductive biases. I think it’s fair to count the hundreds of millions of years of evolution between the first animals with a primitive neural system and humans today, and the amount of “tokens” that were “trained on” is likely enormous. I think it’s better to consider the 200M tokens as a sort of “fine tuning” to a sort of genetic “pre-training”.
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TGower
> Our new paper (Baron et al., 2026) finds that when the dataset is held fixed, the compute-optimal model size keeps growing with the compute budget.
The dataset is not held fixed in practice. It would be interesting to see an ablation study of where best to allocate compute between more synthetic data, more RL rollouts, and this paper's suggestion of cranking up the parameter count. Unfortunately that's an experiment only a frontier lap could accurately do, and they are unlikely to provide such expensive and valuable information to their competitors.
> Humans, by contrast, have ~100T parameters (counting each synapse as a weight) and learn from just ~200M tokens, a tokens per parameter of 1:500,000.
I don’t think that’s a fair comparison since (as far as I know) humans aren’t born with randomly initialised neural weights that are setup in a very generic structure, but have at the very least extremely strong inductive biases. I think it’s fair to count the hundreds of millions of years of evolution between the first animals with a primitive neural system and humans today, and the amount of “tokens” that were “trained on” is likely enormous. I think it’s better to consider the 200M tokens as a sort of “fine tuning” to a sort of genetic “pre-training”.
> Our new paper (Baron et al., 2026) finds that when the dataset is held fixed, the compute-optimal model size keeps growing with the compute budget.
The dataset is not held fixed in practice. It would be interesting to see an ablation study of where best to allocate compute between more synthetic data, more RL rollouts, and this paper's suggestion of cranking up the parameter count. Unfortunately that's an experiment only a frontier lap could accurately do, and they are unlikely to provide such expensive and valuable information to their competitors.
The planet has 20 quadrillions of ants
Paper: https://arxiv.org/abs/2610.37842
Yes synapses are weights, you know science, you smart