OpenAI Researchers Link Training Choices to Reduced Reasoning Transparency

OpenAI’s safety team analyzed models from GPT-5 through GPT-6 Astra and linked four training factors to reduced chain-of-thought transparency: base-model capability, architecture, reinforcement learning, and transfer from teacher models. The analysis found that some architectures gain more from silent multi-step reasoning, while additional reinforcement learning improves controllability in Astra-class models. OpenAI’s researchers described a tradeoff: longer, more visible reasoning can be easier to monitor, but it increases compute costs in deployment.

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