Hacker News Reader

Comment by paytonjjones | Hacker News Reader

Maybe not AGI, but if you look at the differences between, say, GPT-2 and GPT 5.5, it's remarkable how well it works to mostly just throw scale at the problem.

View on HN

The difference is a lot more than just throwing scale at it, pretty much everything useful comes from an evolving landscape of post-training techniques.

Of course, param count and context length are also important because they increase the model's overall fidelity, but a base model without SFT, RHLF etc is effectively useless.

Correct. That is what I was trying to hint at. Yes, massive compute is needed to train ai, but it isn’t the only thing. A lot of research and experimentation goes into moving the marker just a little bit. Innovation can’t be forced into weekly sprints, it takes its own time.

Research and experimentation on neural nets has been going on since the 70s (arguably much earlier even), but the lions share of capability changes has all been in the last couple years.

Scale was really the unlock; the new pre and post training techniques and architectures are very cool and useful but they definitely aren't the differentiators when comparing to the previous era of NLP.

which non-transformer neural networks are matching frontier performance using compute scale?

BERT is a transformer! The unlock happened within transformers, yes, but they were not exactly super new or innovative architectures at that time. The scale was the main innovation that brought us from BERT to today's frontier.

I think the “unlock” is that AI firms were given trillions of dollars to discover new techniques. In fact, there are very few industries where a sudden influx of that much money would not lead to rapid advancements. It’s not really unique to the AI field.

Your timeline is a bit backwards: they were given trillions of dollars only after they'd demonstrated a few pretty remarkable advancements.

The fact that their advancement suggested that pouring more compute would continue working was also especially attractive to investors: it made a massive R&D budget feel like less of a risk.

I think the unlock only happens once though. I think that's where people are misled at the moment, the technology was there but required huge compute and data ingest to show improvement, but we have done that now. What's next for a giant leap is not more compute, and what new data we can provide now pales in comparison to that first ingest.

I was under the impression that the deltas between versions were shrinking- i.e. gpt 4 -> 5 was much less impactful than 3 -> 4 or 2 -> 3. If the growth is getting diminishing returns, I can't say I'm optimistic without finding a drastically different approach.

GPT 4/5 etc. are just marketing names though.

They already tried that with GPT-4 and GPT-4.5

They were allegedly massive but the cost and returns were not worth it.

Not really. I didn't use GPT-2, but I don't think there was much difference between we got what out of GPT-3.5 and 5.5. It's still an unreliable tool which will go off the rails the second you aren't watching it like a hawk. It still has zero intelligence or ability to reason as to why its output is flawed. Just throwing more compute hasn't gotten us anything worth using.

Reply on news.ycombinator.com