GPT-6 Astra, looped transformers, and hidden reasoning
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GPT-6 Astra, looped transformers, and hidden reasoning
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Re: GPT-6 Astra, looped transformers, and hidden reasoning
#2Everyone 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 reuse the weights and so save GPU memory. It's still just producing one token at a time, and the token sequence positions aren't interacting in any "recurrent" way that's different from a regular LLM architecture.
So, you can still monitor train of thought with these models just fine... well, if you're OpenAI, anyway. Users haven't been able to see an unsummarized trace since o1 days, because the labs are worried about distillation of their models by Chinese labs.
(There are some legitimate interpretability concerns about stacking transformer layers endlessly, but we're known about that for a long time. And the "looping" here isn't really the source of any new issues here, except insofar as it's a cheap way to add more layers.)
[0] https://www.theinformation.com/articles/secret-technique-beh...
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#3Everyone 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…
>made it sound like it was some special new scary thing that made train-of-thought monitoring harder to do.
It's not a "scary new thing" but ultimately no-one knows exactly how OpenAI have implemented looping. You might not be aware/remember but MoE transformers perennially underperfomed their dense counterparts until GPT-4. Similarly, making reinforcement learning really work with transformers wasn't figured out until o1.
And by Open AI's own admission, Astra's CoT is significantly harder to monitor and it exhibits a significantly greater control over its own CoT than any other model released.
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#4Everyone 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…
>made it sound like it was some special new scary thing that made train-of-thought monitoring harder to do. It's not a "scary new thing" but ultimately no-one knows exactly how OpenAI have implemented looping. You might not be aware/remember but MoE transformers perennially underperfomed their dense counterparts until GPT-4. Similarly, making reinforcement learning really work with transformers wasn't figured out unt…
"Don't worry, it'll make us rich -- and that's nearly the same as everything being just fine"
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#5This article is not up-to-date. There have been various benchmarks (some of which published and acknowledged by OpenAI, see the charts in this thread: https://xcancel.com/tomekkorbak/status/2095596839886274689) showing GPT-6 Astra is much less monitorable. The most recent third party benchmark I saw is showing a huge jump in capability for multi-hop reasoning without chain of thought: https://www.lesswrong.com/posts/FsCkkoGsNmPzFKRhg/gpt-6-astr...
I don't think this is explained by the model simply being more capable and therefore achieving more per token: the usage of recurrent depth (Neuralese) is exactly predicting less CoT monitorability even at equal capability.
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#6Everyone 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…
If the agent is able to “decide” when a loop should occur vs when an output token is produced, that effectively moves the CoT inside the architecture. While that’s not what is happening here, it’s clearly a plausible way we could see CoT disappear.
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#7Everyone 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…
>made it sound like it was some special new scary thing that made train-of-thought monitoring harder to do. It's not a "scary new thing" but ultimately no-one knows exactly how OpenAI have implemented looping. You might not be aware/remember but MoE transformers perennially underperfomed their dense counterparts until GPT-4. Similarly, making reinforcement learning really work with transformers wasn't figured out unt…
Well sure, that's the possible weak point in Sebastian's article: it could be true that there's some more sophisticated stuff going on in Astra around looping, because OpenAI haven't specified their architecture. But it's always been true that, since we don't know what's in their black box, there could be arbitrary amounts of innovations inside the models that we could speculate about. So the question is, does knowing they use "looped transformers" really add any dramatically new information that we should worry about? And what this article is saying is, not really, because the mostly likely pattern that's referring to is just, effectively, stacking layers and reusing weights.
> And by Open AI's own admission, Astra's CoT is significantly harder to monitor and it exhibits a significantly greater control over its own CoT than any other model released.
Oh sure; I don't think anyone is denying that larger issue? But does it have anything to do with looping?
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#8Everyone 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 interpreter). Dynamically looped transformers can run any program between tokens.
The safety argument for CoT monitoring is that in transformers information about the hidden state has to be communicated through the bottleneck of sampling a single token per forward pass. If not trained adversarially, it’s likely that a reasoning trace contains all the “bottlenecked information” we need to determine intent. But if we can compute arbitrary programs between tokens, the reasoning used is hidden.
It also opens the door to simple architectural extensions that would make the safety/monitoring side of things much more difficult.
It’s probably fine in practice at these scales though. If we keep each loop turn reasonable non-deep, we can probably recover most of the benefits by decoding “extended” CoTs from the residual stream at each loop turn between tokens. But that’s an area of active development.
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#9Probably off-topic. Astra has been kind of weird. Like, I can't trust it, weird. It has an interesting tone, especially in Codex, that is off-putting. It's over zealous at times (which is why I stopped using Claude) and gets too creative when doing agentic system level stuff. Accessing files and doing things it shouldn't do. If OpenAI was chasing Claude's approach, then they are going in the wrong direction. OpenAI has always been the "business and boring approach", which was its selling point and why I have stuck with it. Claude was always the radical one (powerful, but radical).
Also, Astra overlooked, in my opinion, a serious flaw in its approach for something I was working on recently, which really surprised me.
Reading between the lines, there were some breakthroughs with Astra, which I'm sure is why OpenAI released it so quickly after Sol, but probably not in the ways the traditional OpenAI customer wanted.
Re: GPT-6 Astra, looped transformers, and hidden reasoning
#10Probably off-topic. Astra has been kind of weird. Like, I can't trust it, weird. It has an interesting tone, especially in Codex, that is off-putting. It's over zealous at times (which is why I stopped using Claude) and gets too creative when doing agentic system level stuff. Accessing files and doing things it shouldn't do. If OpenAI was chasing Claude's approach, then they are going in the wrong direction. OpenAI h…
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 the metered tasks I don't see the utility.
This is a problem for OpenAI because if Sol is good enough, and they don't have a moat, then it's only a matter of time before Sol-level models are open sourced and running locally. I know I'll be doing that as soon as I can.