OpenAI studies what affects CoT monitorability across the model lifecycle

OpenAI researchers examined how pretraining, architecture, reinforcement learning and model-generated data affect two proxies for chain-of-thought monitorability: no-CoT capability and CoT controllability. They report that no-CoT capability tracks lower base-model perplexity smoothly, that CoT controllability rises during RL even without process supervision beyond length constraints and final-answer grading, and that controllability can be passed to a student model through SFT on a teacher's rollouts. The authors caution that both proxies are imperfect and that models may become harder to monitor through their CoT as capabilities scale.