Transparency about the opaque serial depth is great, but this statement is consistent with Astra having a configurable "dial" that is currently set to a low depth but could be trivially increased. We need more info to see how concerning these architectural changes are, including: - Are there readily available ways to deploy this AI with much higher serial depth (that would be commensurately more performant)? This should include things like tiny amounts of fine-tuning to productively increase the number of iterations. - Is the AI a large or above-trend jump in opaque reasoning capabilities? (Capabilities within a single forward pass or ability to subvert a CoT monitor.) (If there are in fact any relevant changes—perhaps the reporting is inaccurate?) Additionally, I worry that this architectural change will naturally lead to much more depth in the future if this direction is pursued further. Specifically, I wonder: - Does the AI have an architectural change that makes it much more natural to massively scale up the depth in a future training run with a similar architecture? As in, does the architecture introduce some new depth/recurrent-iterations parameter that is very natural/performant to massively scale up relative to scaling up other things like width? The details of the answers to these questions matter. E.g., if there are only a few (recurrent) iterations and you could scale up the number of iterations, but this wouldn't be particularly performant/natural with this architecture, then this development would be a lot less concerning!Separately, it's useful to get some understanding of what high-serial-depth techniques are under active development (at both OpenAI and other AI companies), even if these methods aren't applicable to Astra. (While this is important, this is a basically separate and higher stakes question for companies to answer.)"productively increase the number of iterations" should say "productively increase the number of recurrent iterations"

Transparency about the opaque serial depth is great, but this statement is consistent with Astra having a configurable "dial" that is currently set to a low depth but could be trivially increased. We need more info to see how concerning these architectural changes are, including: - Are there readily available ways to deploy this AI with much higher serial depth (that would be commensurately more performant)? This should include things like tiny amounts of fine-tuning to productively increase the number of iterations. - Is the AI a large or above-trend jump in opaque reasoning capabilities? (Capabilities within a single forward pass or ability to subvert a CoT monitor.) (If there are in fact any relevant changes—perhaps the reporting is inaccurate?) Additionally, I worry that this architectural change will naturally lead to much more depth in the future if this direction is pursued further. Specifically, I wonder: - Does the AI have an architectural change that makes it much more natural to massively scale up the depth in a future training run with a similar architecture? As in, does the architecture introduce some new depth/recurrent-iterations parameter that is very natural/performant to massively scale up relative to scaling up other things like width? The details of the answers to these questions matter. E.g., if there are only a few (recurrent) iterations and you could scale up the number of iterations, but this wouldn't be particularly performant/natural with this architecture, then this development would be a lot less concerning! ... Separately, it's useful to get some understanding of what high-serial-depth techniques are under active development (at both OpenAI and other AI companies), even if these methods aren't applicable to Astra. (While this is important, this is a basically separate and higher stakes question for companies to answer.) ... "productively increase the number of iterations" should say "productively increase the number of recurrent iterations"