If 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)
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.
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.
Everyone 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.)
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.
>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.
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?
Oh, is the principle of sparse universal transformers finally in SoTA LLMs?
I guess we did manage to eventually seriously crash into the wall "more compute than normal (non-looped/unique-weights) transformers can efficiently consume with the limited training data we have", plus massive focus on highly hands-off agentic tool use reasoning...
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.
> that effectively moves the CoT inside the architecture
This may be a bit of a nitpick, but... does it? I agree that giving the model decisions on looping certainly makes interpretability harder, because it adds more transient internal states to deal with and changes the number of them depending on prior states. But is it really pulling CoT inside the forward pass, if the sequence length it's operating on isn't growing? In some sense the whole technique and tradeoff of CoT is "add more tokens to the sequence, use them to reason with", with one of the benefits being, you force the model to output tokens, so you can (hopefully) understand it. And the big point TFA is making is, nobody is doing recurrence over sequence length as far as we know.
Just "adding more layers" doesn't explain the step change. We have moved past the point you can just stack more layers and get huge gains from it. Some have called it latent space reasoning.
It is worth noting that None performed better than Low and nearly the same as Medium on ARC3. And with adapter it still scored >96% with no CoT. So I think it is possible but it also cost them more on None.
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.
I work on small scale recurrent transformer architectures.
Better Multi hop reasoning is one of the most notable improvements of the architecture. The tricky part is figuring out a way to optimize the number of times you loop as it varies between tasks. Too few and you leave performance on the table too many and performance begins to drop.
Recommended reading from an actual researcher who thoroughly understands AI research papers and has an in depth analysis of models architectures and their mechanics and no nonsense benchmarks.
Probably 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.
>> 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.
I gave Astra a pretty straightforward bug ticket yesterday. The bug involved an edge case that could sometimes result in an invalid value getting stored in a user profile field. Pretty harmless, no crash or anything, just annoying.
Based on past experience, I don't trust OpenAI, so I decided to watch Astra as it worked. About four minutes in, it convinced itself that it should also check the prod database to see "how far the corruption has spread" and attempted to SSH into the hosting provider. This resulted in my 1Password to prompt me, which I of course denied. Then I stopped Astra, closed the ChatGPT/Codex app and gave the task to Opus 5. Suffice it to say I will not be renewing my subscription, because "you have to watch it like a hawk" is the opposite of agentic engineering.
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.
I had a few problems which Sol was bumbling around with and giving mediocre results (e.g. in a toy planet app, Sol was taking several iterations to get a half decent looking render of the weather I still wasn't pleased with) but Astra managed to implement well in one go.
Much the same as you're saying, I never got around to verifying how much of that was because of Astra being better vs just being a different model sent specifically to those tasks because the token usage didn't make sense to spend unless it was something not working in Sol. So even if it was all due to Astra being fantastic I'd still not like to use the model for the cost being even more fantastic.
I'm still working through my first few days, but I've had to deal with Opus ADHD for a while. I built a task management system which is closer to old school remedy with reviewers. The stylistic guidelines on task creation have a seven part problem statement, goal, success, ancillary data and such. By framing the task diligently it does keep the work on target. The review logic is basked into the task management software so the agent can't declare done. On open ended issues it can still wander. It's been remarkable to drive down issues over these last few weeks. I was annoyed I had to stop for 3 days and build management infrastructure, but it's paid for itself.
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.
So the TL;DR here is that Astra's trick is that its a turbo-charged weaker model vs a larger heavier one-pass model -- and the turbo is instead of reasoning by 'talking out loud' and generating intermediary steps, the reasoning is able to be stored (probably as KV) and re-run as purely without the generation of the text. Making it more effecient to run successively and I assume more intelligent as the act of turning the KV cache into lingusitics loses some dimensionality (especitally spacially)
double TLDR: This is a Turbo V4 instead of a huge V8 of a model.
The big thing that was learned all the way back with UT and it's follow up SUT was that semantic nesting structure often incentivizes models that can deploy the very same learned structural parsing intelligence independent of how many layers of nesting had to be unwrapped for this structural pattern to surface.
Think how a reverse polish notation calculator with reasonably limited data stack depth could run efficiently with a plain vanilla transformer.
But if you input classic grade school parenthesized infix with a few levels of operator precedence, you are no longer able to just evaluate the expression during transformer prefill.
Even if you add a stack depth bound worth it reasoning tokens between any two input tokens as they're processed.
UTs can, at least if run with encoder (unmasked) attention, resolve the task through technically-flexible iteration count that can and will follow the evaluation order of the infix operator tokens of the input expression.
While masked attention unfortunately limits it's powers, the fundamental benefit of separating task-specific-intelligence (an individual expert of an MoE) from the notion of which transformer layer has it pre-digested just right for that task/processing to be done to it, allows for massive reduction in model parameter count.
Note this comes at a penalty of parameter activations (inference will take more compute).
It's just that at some point you can't afford to just train more parameters, without suffering overfitting issues/failures-to-generalize.
The architecture decoupling learned weights from when they're activated also helps with generalization to out-of-distribution structures.
Think resilience against yoda-speak and such.
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)
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...
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.
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.
> 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?
I guess we did manage to eventually seriously crash into the wall "more compute than normal (non-looped/unique-weights) transformers can efficiently consume with the limited training data we have", plus massive focus on highly hands-off agentic tool use reasoning...
https://arxiv.org/abs/2310.07096
Edit: read much of the article, it's brute force predecessor was explicitly called out as an almost-ancient example:
> The looped transformer is nothing new, and the basic idea already appeared in the Universal Transformers paper from 2018
This may be a bit of a nitpick, but... does it? I agree that giving the model decisions on looping certainly makes interpretability harder, because it adds more transient internal states to deal with and changes the number of them depending on prior states. But is it really pulling CoT inside the forward pass, if the sequence length it's operating on isn't growing? In some sense the whole technique and tradeoff of CoT is "add more tokens to the sequence, use them to reason with", with one of the benefits being, you force the model to output tokens, so you can (hopefully) understand it. And the big point TFA is making is, nobody is doing recurrence over sequence length as far as we know.
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.
Better Multi hop reasoning is one of the most notable improvements of the architecture. The tricky part is figuring out a way to optimize the number of times you loop as it varies between tasks. Too few and you leave performance on the table too many and performance begins to drop.
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.
I gave Astra a pretty straightforward bug ticket yesterday. The bug involved an edge case that could sometimes result in an invalid value getting stored in a user profile field. Pretty harmless, no crash or anything, just annoying.
Based on past experience, I don't trust OpenAI, so I decided to watch Astra as it worked. About four minutes in, it convinced itself that it should also check the prod database to see "how far the corruption has spread" and attempted to SSH into the hosting provider. This resulted in my 1Password to prompt me, which I of course denied. Then I stopped Astra, closed the ChatGPT/Codex app and gave the task to Opus 5. Suffice it to say I will not be renewing my subscription, because "you have to watch it like a hawk" is the opposite of agentic engineering.
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.
Much the same as you're saying, I never got around to verifying how much of that was because of Astra being better vs just being a different model sent specifically to those tasks because the token usage didn't make sense to spend unless it was something not working in Sol. So even if it was all due to Astra being fantastic I'd still not like to use the model for the cost being even more fantastic.
double TLDR: This is a Turbo V4 instead of a huge V8 of a model.
Think how a reverse polish notation calculator with reasonably limited data stack depth could run efficiently with a plain vanilla transformer.
But if you input classic grade school parenthesized infix with a few levels of operator precedence, you are no longer able to just evaluate the expression during transformer prefill. Even if you add a stack depth bound worth it reasoning tokens between any two input tokens as they're processed.
UTs can, at least if run with encoder (unmasked) attention, resolve the task through technically-flexible iteration count that can and will follow the evaluation order of the infix operator tokens of the input expression.
While masked attention unfortunately limits it's powers, the fundamental benefit of separating task-specific-intelligence (an individual expert of an MoE) from the notion of which transformer layer has it pre-digested just right for that task/processing to be done to it, allows for massive reduction in model parameter count. Note this comes at a penalty of parameter activations (inference will take more compute).
It's just that at some point you can't afford to just train more parameters, without suffering overfitting issues/failures-to-generalize.
The architecture decoupling learned weights from when they're activated also helps with generalization to out-of-distribution structures. Think resilience against yoda-speak and such.