Comment by libraryofbabel
1 day ago
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 sure; I don't think anyone is denying that larger issue? But does it have anything to do with looping?
If the model has significantly more ability to stuff away information outside visible reasoning than every other model including ones in its size class then surely it is reasonable to assume the architecture tweak that allows the model to compute more before outputing a single token is somewhat responsible for this change ?
> If the model has significantly more ability to stuff away information outside visible reasoning
I’m having trouble understanding why you believe the “if” part is true.
Because it is.
From OpenAI
9.2.1 CoT Controllability
We find that GPT-6 Astra’s CoT controllability is substantially higher than that of GPT-5.6 Sol and GPT-5.5 Thinking (Figure 28)....For example, among CoTs between 750 and 1,250 tokens long, GPT-6 Astra successfully controls 60.9%, compared with 16.1% for GPT-5.6 Sol and 1.7% for GPT-5.5 Thinking. This increase in controllability is consistent across the three datasets (Figure 29) and across the eight CoT instruction types (Figure 30). Qualitatively, GPT-6 Astra is now capable of generating very long CoTs satisfying complex constraints, e.g., alternating between lowercase and uppercase letters (Table 9) and pretending to reason about a different question (Table 10).[1]
9.3 External Evaluation for Monitorability - UK AISI
To assess monitorability, UK AISI evaluated Astra using four non-agentic evaluations:
No-CoT math time horizon: Astra can solve significantly more difficult math problems in a single forward pass than past models. UK AISI measured Astra’s time horizon at 30.9 minutes compared to 3.6 minutes for GPT 5.6 Sol (Figure 1). [2]
[1]https://deploymentsafety.openai.com/gpt-6-astra/cot-controll...
[2]https://deploymentsafety.openai.com/gpt-6-astra/external-eva...
Outside OpenAI
Astra has 8.6x better odds of doing a reasoning task without CoT than the next best model (Fable 5.1), and can do 7.2 serial arithmetic steps in a forward pass vs 4.1 for the next best model (Gemini 3.8 Flash/Fable 5.1) [3]
[3]https://www.lesswrong.com/posts/eRmzz8J8Qkzqvzrgg/astra-can-...
[4]https://www.lesswrong.com/posts/ntKx9YHWCwxSeGbRB/estimating...
[5]https://www.lesswrong.com/posts/FsCkkoGsNmPzFKRhg/gpt-6-astr...