OpenAI ties hidden model reasoning to training choices, not scale
First merged summary.
OpenAI says GPT-6 Astra's silent, controllable chain of thought comes from architecture, reinforcement learning, and teacher models, not an inevitable scaling law.
OpenAI's safety team analyzed models from GPT-5 through GPT-6 Astra and found four training factors that make reasoning logs easier to hide or manipulate. Silent reasoning rises with base-model capability and depends on architecture; reinforcement learning and training on teacher models increase control over the chain of thought. Astra still learned scratchpad control without direct penalties on the reasoning log. OpenAI says a weaker model that reasons out loud can match a larger short-chain model and stay easier to monitor, at higher compute cost. According to the UK AI Safety Institute, Astra solves roughly 31-minute human math problems with reasoning off.
- Silent reasoning rises with capability and depends on model architecture.
- Reinforcement learning increased chain-of-thought controllability for Astra-class and Terra models.
- Student models inherit a teacher model's high chain-of-thought controllability.
- Architecture 2 gained faster on silent planning than other designs at equal perplexity.
- UK AI Safety Institute: Astra solves about 31-minute math problems with reasoning off.
- Deception tests did not show high-control models fooling monitors more often.
What each outlet adds
beyond the shared facts- The DecoderOpenAI's four-factor account of silent, controllable reasoning in Astra.
Coverage
oldest first · one row per article · UTC- · 5d ago
The DecoderFirst report
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OpenAI traces silent and controllable chain-of-thought in GPT-6 Astra to architecture, RL, and teacher models, not an inevitable scaling law.