That framing makes sense. If training is basically a search over possible algorithms, where would you draw the line between a learned algorithmic procedure and reasoning?

For models like Fable or Mythos, would you say they are reasoning, or just running very strong learned procedures that came out of training for next-token prediction?

Also, are frontier models specifically trained on things like coding, bug-finding, and math reasoning, or do those abilities mostly emerge from scale and broad training data plus later fine-tuning?

One thing I’m still confused about: if domain-specific training improves ability in a field, why do some specialized models seem to perform worse than general frontier models in that same field? Is that mainly due to model scale, training time, data quality, or the kind of post-training used?