Comment by dingnuts
Comment by dingnuts 4 days ago
> Latent reasoning doesn't really appear until around 100B params.
Please provide a citation for wild claims like this. Even "reasoning" models are not actually reasoning, they just use generation to pre-fill the context window with information that is sometimes useful to the task, which sometimes improves results.
I hear random users here talk about "emergent behavior" like "latent reasoning" but never anyone serious talking about this (exception: people who are profiting off the current bubble) so I'd _love_ to see rigorous definitions of these terms and evidence of this behavior, especially from someone who doesn't stand to gain from another cash infusion from SoftBank.
I suspect these things don't exist. At the very most, they're a mirage, and exist in the way a rainbow does. Go on and try to find that pot of gold, eh?
> Please provide a citation for wild claims like this. Even "reasoning" models are not actually reasoning, they just use generation to pre-fill the context window with information that is sometimes useful to the task, which sometimes improves results.
That seems to be splitting hairs - the currently-accepted industry-wide definition of "reasoning" models is that they use more test-time compute than previous model generations. Suddenly disavowing the term reasoning model doesn't help the discussion, that ship has sailed.
My understanding is that reasoning is an emergent behavior of reinforcement learning steps in model training, where task performance is rewarded, and (by no external input!) the model output starts to include phrases ala "Wait, let me think". Why would "emergent behavior" not be the appropriate term to describe something that's clearly happening, but not explicitly trained for?
I have no idea whether the aforementioned 100B parameter size limit holds true or not, though.