Astra was insane until Monday but something happened on tuesday, now it feels like Sol. I grieve for the lost productivity but i hope they may give us the original Astra back.
Which plan/region are you on/in?
GPT-6 Astra, looped transformers, and hidden reasoning
41–50 of 152 posts
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#42Earlier quoted context omitted.
I have tested it out with CAD and PCB circuits and it is a huge jump compared to Sol. I agree though when trying it with programming I don't notice a huge jump.
I'll have to try it for a PCB circuit because that's where I'm going next. Were you asking it to use specific software to build the circuits?
Using KiCad by uploading their _sch and _pcb files. Originally with Sol, I stuck to using it for finding parts and double checking my KiCad schematic. Definitely good at finding parts quickly from JLCPCB's stock and for quick cosmetic edits of the schematic. I found its PCB editing abilities pretty bad, though it was useful for cosmetic edits (quickly relabeling silkscreen labels) and for creating a nice custom DRU file. With Astra on the other hand it can actually make good PCB edits. Still not great but usable and editing it quicker than starting from scratch. I do doubt you can go 0-100 with just Astra but definitely sped up my work. For reference my circuits are high amperage, noise sensitive, and interface with sensors. They are pretty simple circuits though, just fairly simple ICs with no MCU or anything like that.
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#43If you loop an entire transformer model on itself, that seems like by-definition hidden reasoning. If the output of the model is its reasoning trace, and you simply feed that back into the model again at inference time instead of outputting it - then it is by definition hidden (but I would expect you could pull both this trace and a further-down final output trace out)
No. It’s not at all by definition hidden reasoning.
Looping transformers uses additional calculations (repeating layers) to generate a token.
Reasoning (in this context) is test time generation of multiple tokens that allow a model to have a scratch pad to refine its thoughts, chain of thought reasoning in other words.
Doing the former in no way means that you have to hide the latter.
Raschka is right in this post, The Information article was wrong. The Astra system card does concede reasoning traces are sometimes smaller, but this could be for a lot of reasons, including simple efficiency. And it absolutely doesn’t mean they are going away or completely obscured.
The Last Week in AI podcast from Sept 8 seems to have gotten this wrong as well. Jeremie Harris rages that OpenAI implemented latent reasoning, ala the coconut paper, which could potentially actually obscure reasoning traces. But for the life of me, I do not know how he arrived at this conclusion and see no evidence that this has happened in Astra.
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#44Everyone interested in LLM internals should read Sebastian. He's great. The tldr here is that the recent "The Information" article[0] reporting GPT 6 Astra was using “recurrent depth” or “looped transformers" made it sound like it was some special new scary thing ("secret technique!") that made train-of-thought monitoring harder to do. In fact, it's just the same as stacking more transformer layers, except that you r…
It’s a little more complicated than that. While looped transformers can be unrolled a fixed number of times to save on memory, if loop depth is determined dynamically between tokens, a single transformer can compute any computable function between tokens. To analogize, current transformers run a fixed-length program per step. Any program can be factored into a top-level loop with a fixed-length branching body (an int…
Thanks for clarifying. You're right, I skipped over talking about dynamic looping, since it adds another level of complexity to the discussion, and OpenAI's claim (quoted in TFA) that the compute graph depth of Astra is "within a factor of two of GPT-4" basically denies that they're doing it for more that 1 (or maybe max 2) dynamic loops. And that is equivalent to "stack repeated layers a couple times, but with dynamic off-ramps."
The ability to compute any computable function between tokens given an ability to loop an arbitrary number of times is a nice theoretical point, sure, but ultimately if people are still using single digit hard cutoffs on the number of loops, I'm not sure it's all that important.
So, I agree it's right to say that arbitrary length dynamic looping could open the door to making monitoring very hard indeed, by extending hidden states further and further. But I would speculate that if it actually worked better than extending the sequence with CoT tokens, we'd already be seeing it in strong open weight models. It's a fairly obvious thing to try. And we're not seeing it, AFAIK. So I do wonder whether it's something we really need to worry about in practice, compared to all the other things we have to worry about.
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#45Astra was insane until Monday but something happened on tuesday, now it feels like Sol. I grieve for the lost productivity but i hope they may give us the original Astra back.
It's the same story every time OpenAI or Anthropic releases a new model. They are generous with compute for the first few days, and use maximum fidelity with uncompressed weights. Everything runs at its best to make a good first impression. But eventually they pare things back and the models perform a little worse.
You think they introduce stronger quantization after a few days?
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#46Astra was insane until Monday but something happened on tuesday, now it feels like Sol. I grieve for the lost productivity but i hope they may give us the original Astra back.
I had nearly the exact same experience and thought I was imagining it … absolutely ripping, then it turned into Sol++ on Tuesday …
I’m working on hard things, it is very noticeable when it is hums through something and then falls over on something it should not
I can tell by analyzing my own prompts to look at when I get frustrated ;)
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#47Earlier quoted context omitted.
It's the same story every time OpenAI or Anthropic releases a new model. They are generous with compute for the first few days, and use maximum fidelity with uncompressed weights. Everything runs at its best to make a good first impression. But eventually they pare things back and the models perform a little worse.
Or a lot worse
so, AGI is cancelled?
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#48Earlier quoted context omitted.
I don't really like Astra either. It doesn't seem noticeably better than Sol, and it uses more tokens. Some people said ultimately it's cheaper because it can solve problems faster but I haven't really noticed that. The way I use it now is I'll ask a chat 6 Pro session to make a plan and then have Sol implement it, then 6 Pro reviews it. This seems fine and it doesn't use my Codex minutes, so I'll use Astra. But on t…
I have tested it out with CAD and PCB circuits and it is a huge jump compared to Sol. I agree though when trying it with programming I don't notice a huge jump.
Anecdotally (I did try it myself, but wasn’t blown away) many seem to like it for 3D modelling. That was emphasized in the promo too. I think this kind of ”general intelligence” is what is meant to set it apart from 5.6.
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#49Re: GPT-6 Astra, looped transformers, and hidden reasoning
#50Everyone interested in LLM internals should read Sebastian. He's great. The tldr here is that the recent "The Information" article[0] reporting GPT 6 Astra was using “recurrent depth” or “looped transformers" made it sound like it was some special new scary thing ("secret technique!") that made train-of-thought monitoring harder to do. In fact, it's just the same as stacking more transformer layers, except that you r…
It’s a little more complicated than that. While looped transformers can be unrolled a fixed number of times to save on memory, if loop depth is determined dynamically between tokens, a single transformer can compute any computable function between tokens. To analogize, current transformers run a fixed-length program per step. Any program can be factored into a top-level loop with a fixed-length branching body (an int…
By that logic we should also consider the case of cutting the number of layers in half because that would also reduce hidden state between token generation.
In your generalized example I think the concern is when the additional evaluation effectively becomes a replacement for CoT, where something like the coconut research could replace it completely.
However, I don’t think we’re anywhere close to that with Astra.