OpenAI Researchers Link Training Choices to Reduced Reasoning Transparency

1. 00:58
   The DecoderPicks

   OpenAI Analysis Links Frontier Models’ Hidden Reasoning to Training Choices

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

Trends compare the same continuously observed participants, which may cover fewer sources than current heat. Point or click to inspect hourly heat, or use left and right arrow keys.