OpenAI: What affects chain-of-thought monitorability across the model lifecycle?

OpenAI studied how pretraining, architecture, reinforcement learning and model-generated data affect chain-of-thought (CoT) monitorability, using no-CoT capability and CoT controllability as proxy metrics for monitor-evasion capability. Results show that lower base model perplexity steadily raises no-CoT capability, and some architectures exceed perplexity's prediction on multi-hop planning and serial computation evaluations; with reinforcement learning, even with only length constraints and final-answer scoring, CoT controllability rises substantially, while adding tool supervision increases it only slightly. The team also found a subliminal learning-like effect, where CoT controllability can be passed to the next-generation model through model-generated SFT data. The research background is that GPT-6 Astra's CoT monitorability declined in non-adversarial settings, and UK AISI measured its no-CoT math time horizon median at 30.9 minutes, higher than the previous estimate of about 7 minutes by the end of 2028.