Deep dive into Astra's "recurrent depth" architecture trade-offs

daniel_mac8 · x · 2026-09-02

Elie Bakouchi shares technical thoughts on OpenAI Astra's "recurrent depth" approach:

Speed: It is not inherently faster at inference or training than scaling depth, as the "effective depth" remains.
Key Benefit: The primary advantage is KV cache storage optimization (achievable via KV sharing).
Use Case: May help in undertrained regimes or convergence, though frontier models likely don't need it.
Adaptive Compute: Allows skipping recurrent blocks, similar to layer skipping or MoE routing.

Separately, concerns were raised that this architecture obscures the model's thinking process, complicating monitoring.

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