Monitorability Is Trainable: Post-Training for Chain-of-Thought Monitorability

Chain-of-thought (CoT) monitoring is one of the few practical tools available for overseeing capable AI systems, but it is fragile: a model's reasoning is only useful to a monitor if it remains coupled to what the model actually does. This coupling can be incidentally degraded under ordinary post-training through CoT compression, through accidental optimization against a monitor, and through out-of-context generalization from synthetic documents. In this work, we propose an unsupervised post-training objective based on maximizing mutual information between CoT and the output for improving CoT monitorability. Across three model organisms with degraded CoT, it recovers most or all of the lost monitorability. Applied to undegraded policies, it further improves judged monitorability and increases the prevalence of consequential reasoning steps. These results suggest that monitorability should be treated as a trainable attribute of the policy. A dedicated monitorability post-training stage can extend current practices that protect CoT from direct optimization pressure.