OpenAI’s latest model Astra leverages ByteDance’s architecture, which involves repeated computation of the same Transformer layer.
6 days ago
Beating AI News Brief: OpenAI’s upcoming Astra will use a recurrent reasoning architecture called recurrent depth, also known as looped transformer. Standard Transformers process each token by sequentially passing through fixed layers, while Astra allows the same information to repeatedly cycle through the same set of layers for multiple computation rounds before generating an output. ByteDance already publicly demonstrated a similar approach last year: its Seed team released Ouro, a Looped Language Model that runs a set of Transformer layers cyclically to offload more computation inside the model. This architecture boosts inference computation without requiring the model to generate longer chains of thought (CoT), enabling smaller models to leverage extra computation to achieve performance close to that of larger models. However, this design introduces a security risk: some inference occurs within internal hidden states, leaving no full textual record, making it harder to verify model compliance via CoT checks. In response, OpenAI has limited the extent of recurrent depth used in Astra to retain readable CoT, and plans to add additional CoT monitoring.
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